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Fracture conductivity – Design considerations and benefits in unconventional reservoirs
Journal of Petroleum Science and Engineering 124 (2014) 407–415
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Journal of Petroleum Science and Engineering journal homepage: www.elsevier.com/locate/petrol
Fracture conductivity – Design considerations and benefits in unconventional reservoirs Mark Chapman, Terry Palisch n CARBO Ceramics Inc, USA
art ic l e i nf o
a b s t r a c t
Article history: Received 21 August 2013 Received in revised form 10 September 2014 Accepted 13 September 2014 Available online 28 September 2014
Hydraulic fracturing is critical to the success of the petroleum industry in the twenty-first century, turning previously uneconomic reservoirs into success stories through the application of horizontal wellbores and multiple transverse fracture treatments. The ability to contact large volumes of rock in these ultra-tight reservoirs through a single horizontal wellbore has allowed for reduced cost of drilling when compared to multiple vertical wells. While most engineers tend to understand the wellbore and its components, the hydraulic fracture treatment is typically less understood. However, the wellbore and the hydraulic fractures are expected to provide a conductive flow path to the reservoir for the life of the well, so the comprehension of each are critical to the long term viability of the completion. Of course when it comes to the hydraulic fracture, the proppant is the material that is expected to provide this long term connectivity (a.k.a. conductivity), so its selection should be treated the same as the selection of wellbore components – it should be chosen by looking at all phases of the well's production, including the service when the well is first placed on production, the environment if/when the well is placed on artificial lift, and the conditions when the well is producing 20 years later. This paper will present a thorough review of fracture conductivity principles, with particular emphasis on the impact that downhole conditions has on its realistic measure. After reviewing the primary parameters that will impact fracture conductivity, the authors will review the main components of proppant selection, specifically as it relates to the process in unconventional reservoirs over the last several years, and will present a methodology which will lead a design engineer to the optimal fracture design. Finally, case histories which illustrate the application of these theories will be presented for actual completions in both the Bakken and Eagle Ford plays. This paper will be beneficial to those engineers and technologists who are interested in designing optimal completions in their wells and maximizing the return on their investment. & 2014 Elsevier B.V. All rights reserved.
Keywords: hydraulic fracturing completion design proppant selection case histories
1. Introduction Hydraulic fractures have historically been placed for one of two reasons; to accelerate production or to increase the recovery. In many “conventional” reservoirs with sufficient permeability to flow on their own, fracture treatments are designed to accelerate the production rate and thereby improve the present value of the well and reserves. Conversely, in today's unconventional reservoirs, engineers are primarily placing fracture treatments to drain formations that, due to their extremely low permeability, cannot economically produce hydrocarbons from a single, unstimulated
n
Corresponding author. E-mail addresses: [email protected] (M. Chapman), [email protected] (T. Palisch). http://dx.doi.org/10.1016/j.petrol.2014.09.015 0920-4105/& 2014 Elsevier B.V. All rights reserved.
vertical wellbore. By employing horizontal wells and multiple fracture treatments, these wells become economic and add significant reserves. Adding reserves in unconventional plays is dependent on increasing reservoir contact and enhancing flow capacity (conductivity). Reservoir contact is affected by lateral length, stage count, fracture height and fracture half-length. Engineers are able to control fracture height and half-length with fluid rate, volume and viscosity. However, reservoir contact is not beneficial on its own, as sustained flow capacity (or fracture conductivity) must be provided in these fractures so that the link between the reservoir contact and the wellbore remains viable long after the frac pumps and equipment have left the wellsite. Sustained fracture conductivity is primarily governed by proppant volume and selection. Characteristics of proppant that affect conductivity include proppant size, proppant type/quality, proppant concentration/mass (fracture width), proppant durability, fluid clean
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MSCFD ml/min ΔPfrac
Nomenclature API
β
BO BOE KCl kfrac lb/ft2 mD
American Petroleum Institute Proppant beta factor Barrels of Oil Barrels of Oil Equivalent Potassium chloride Fracture permeability Pounds per square foot MilliDarcies
ρfluid TVD
μfluid
vfluid wfrac Xfrac
up, embedment/spalling, and fines migration. The remainder of this paper will discuss these characteristics, their impact on fracture conductivity and ultimately their impact on production rate, EUR and the economics of the well.
2. Conductivity In unconventional/resource plays, millions of dollars are typically required to drill and complete the horizontal wellbores, install isolation systems, place surface production equipment and pump hydraulic fracture treatments in multiple stages. The fracture conductivity placed via the hydraulic fracture treatment is critical since without a sufficiently conductive pathway for hydrocarbon to flow through the fracture to the wellbore, the rest of the aforementioned equipment is either wasted or suboptimal. Fracture conductivity (Cf) is a function of the permeability of the fracture and the fracture width, and is represented by the following equation: C f ¼ kf rac wf rac
ð1Þ
Conductivity is most commonly reported as the baseline or reference conductivity for each proppant size and type. Baseline conductivity is obtained through the “long-term” conductivity test as set forth by the International Organization for Standardization (ISO), under ISO-13503-5. In 2008, the American Petroleum Institute also adopted this standard under API-RP-19D. The ISO/ API conductivity procedures are designed to give a reasonably repeatable determination of the flow capacity of the proppant pack under stress. Some significant details of the test are: (1) utilizes the Cooke Conductivity Cell (Fig. 1); (2) proppant is loaded into the conductivity cell at 2 lb/ft2; (3) proppant is placed
Thousand standard cubic feet per day Milliliters per minute Pressure drop in the fracture Fluid density True vertical depth Fluid viscosity Fluid velocity Fracture width Fracture half length
between 5 million psi Young's Modulus Ohio Sandstone shims; (4) the test is performed at 150 1F for sand and 250 1F for ceramic proppant; (5) stress is increased at a uniform rate and then held for 50 h at the target stress prior to testing; (6) 2% KCl fluid is circulated at an extremely low rate of 2 ml/min and (7) Darcy's Law is used to calculate the permeability and conductivity of the proppant pack. The ISO/API test is designed to allow impartial comparison between proppants under controlled conditions to generate repeatable results. However, API-RP-19D states that results from long term conductivity testing are “not intended for use in obtaining absolute values of proppant pack conductivities under downhole reservoir conditions” (API-RP-19D, 2008). Although this test does not reflect all downhole conditions, it does holistically account for proppant size and strength (crush), it measures pressure drop of a fluid flowing through the pack, and does account for some temperature and embedment reductions. However, since the test doesn't account for many conditions that exist downhole such as non-Darcy and multiphase flow, reduced proppant concentration and flow convergence to name a few, additional tools and methodology are required to accurately predict realistic conductivity, and properly select the optimal proppant for a given completion.
3. Proppant selection The proppant in the fracture is the critical link connecting the hydrocarbons in the reservoir to the wellbore and sales meter. Unfortunately proppant selection is often simplified to single parameters such as depth, stress and crush, or the notion that in extremely tight rock any proppant is good enough. In some cases, proppant selection is merely limited to what the last engineer did, or perhaps what the operator next door is using. However, as engineers we have the ability and perhaps even the obligation to do better. In reality, proppant selection requires the evaluation of multiple factors to optimize for flow performance and economics. Four factors should be considered when selecting proppant in unconventional reservoirs – proppant availability, fracture fluid selection, conductivity requirements and cost-benefit analysis. 3.1. Proppant availability
Fig. 1. Cooke Conductivity Cell used in the measurement of baseline conductivity per ISO-13503-5 (International Organization for Standardization, 2006).
As the development of unconventional reservoirs gained momentum in the mid-2000s, proppant demand increased dramatically, with annual worldwide usage increasing from 5 billion pounds in 2004 to 60–70 billion pounds in 2011, or a 12– 14 fold increase (Palisch et al., 2012). This increased demand had a profound impact on proppant selection, and in many cases led to simply a “bring whatever is available” mentality. To absorb this additional demand, new sand mines of varying quality were quickly opened, new resin coated sand facilities were quickly
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reservoirs require more fracture conductivity than a gas well, leading engineers to select larger mesh proppants such as 20/40 and 30/50. This has led to a subsequent evolution of frac fluid systems in unconventional liquids-rich reservoirs, namely the hybrid fluid design. A hybrid fluid design involves using a low viscosity fluid in the pad and early proppant stages and then as proppant concentrations are increased and/or larger mesh proppants are employed, a higher viscosity fluid (and XL) is used. These fluids are necessary to place larger diameter proppants. 3.3. Conductivity requirements
Fig. 2. Cross-section comparison between various poorly manufactured ceramic proppants, and a high quality ceramic proppant (indicated by red star). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
commissioned, and new ceramic proppant plants were constructed or other industry plants were reconfigured for the manufacture of ceramic proppant. In addition to a shortage of higher tier proppants, this extreme demand led to an influx of large volumes of lower quality proppant in all proppant tiers. Sieve distribution, shape and strength are often sacrificed with sand from inferior mines/ deposits. Premium resin coated sand also requires premium white sand as the substrate, and competes for the same high quality sand deposits. When premium sand is unavailable, then lower quality sands are coated. Ceramic plants require more capital and experience, and take longer to build and develop the expertise to consistently manufacture a high quality ceramic proppant. Ceramic proppant quality is dependent on raw materials, process control and proper firing methods. If high standards of quality control are not held, pellets will develop characteristics that harm conductivity (Fig. 2) such as; internal porosity (lower strength), rough surfaces and, irregular shapes. 3.2. Frac fluid selection With the initial development of the Barnett Shale, fracturing fluid selection moved away from traditional high viscosity crosslinked (XL) gel systems to low viscosity slickwater (SW) or linear gel (LG) systems. High viscosity XL systems are used for many reasons, including improved proppant transport, fracture width generation and planar fracture development. Low viscosity SW/LG systems are used when complex fracture systems are desired, as well as to minimize fluid damage and in some cases reduce costs. However, SW/LG fluids also require that smaller proppants (e.g. 40/70 and 100-mesh) be employed since these smaller diameter proppants are easier to place in the narrower fractures, as well as transport in the fracture. In reservoirs such as the Barnett Shale, where the rock contains an existing cross-cutting fracture system and relatively isotropic horizontal stresses, the development of a complex fracture network is desirable, and in many cases offsets the lower conductivity fractures they create. Complexity creates more reservoir contact, which can be beneficial in many shale gas reservoirs in which hydrocarbons cannot flow readily through the rock on their own. In 2011, shale gas development slowed due to an extended period of depressed natural gas prices and with oil prices in excess of $100/BO, focus shifted to liquid rich resource plays such as the Bakken, Eagle Ford and Utica Shale. Due to the higher viscosity producing fluids and multiphase flow, wells in oil and condensate
Fracture conductivity provides the vital link between the contacted reservoir and the wellbore, and the amount of conductivity required to optimally drain the reservoir is dictated by the deliverability of the rock. Reservoir deliverability is driven by two parameters – reservoir contact and reservoir permeability. While the required fracture conductivity is proportional to the reservoir deliverability, and in many cases the magnitude of the reservoir contact and/or permeability are difficult to determine, the focus of this section the impact of determining an accurate estimate of fracture conductivity. As discussed earlier, realistic fracture conductivity differs from baseline conductivity in that it includes downhole conditions such as non-Darcy and multi-phase flow, lower proppant concentrations, flow convergence in transverse fractures, etc. Often referred to as “realistic conductivity”, the conductivity at realistic conditions is typically found to be orders of magnitude lower than baseline conductivity (Palisch et al., 2007). Each proppant type and size is affected differently by these conditions, and typically requires the use of sophisticated fracture models to determine a proper estimate of realistic conductivity and subsequent proppant selection. The ISO/API baseline conductivity test is run at Darcy conditions (2 ml/min) which are not representative of realistic flowing conditions. If a well were to produce at baseline conditions, it would be equivalent to 6 BPD in a fully perforated vertical oil well with a 50 ft tall bi-wing frac achieving 2 lb/ft2 concentration, or 15 MSCFD flowing at 1500 psi and 250 1F in a similar dry gas well. Increasing velocities in the fracture to more realistic production rates results in additional pressure losses that are not addressed in Darcy's Law. Forchheimer's Equation (Eq. (2)) modifies Darcy's equation to account for both the Darcy pressure losses (mv/k), which are equally controlled by fluid viscosity and velocity and fracture permeability, and the non-Darcy pressure losses (βρv2) which are dominated by the exponential velocity term (Forchheimer, 1901).
ΔP f rac X f rac
¼
μf luid νf luid kf rac
þ βρf luid ν2 f luid
ð2Þ
The baseline conductivity test also utilizes a single-phase 2% KCl fluid. Unfortunately, single phase flow rarely exists in actual flowing fracture conditions. Actual wells consist of a combination of oil, gas and water, all of which compete for space to flow, causing a complex flow regime that leads to much higher pressure drop in the fracture than what is measured in the baseline test. This multiphase flow is typically demonstrated as a non-Darcy effect multiplier due to its nature of having greater affect as velocities increase (Fig. 3). Lab data by various researchers confirm that pressure losses increase even when small percentage of a second phase is included. One of the well completion goals in most unconventional reservoirs is to place numerous transverse bi-wing hydraulic fractures within one single horizontal wellbore, thereby increasing the reservoir contact of a single wellbore by orders of magnitude over a fractured vertical well (Vincent, 2011). However, wells completed with multiple transverse hydraulic fractures create
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another challenge. Fluids flowing through these massive transverse fractures must all eventually converge on the single, relatively small diameter wellbore (Fig. 4). This can lead to significant pressure drop in the near-wellbore region of the fracture. Unlike conventional vertical wells in which the fluids in vertical bi-wing fractures can generally flow directly into the wellbore, the fluids in horizontal transverse fractures must eventually flow radially into the wellbore. As fluids converge on the relatively
Fig. 3. Small fractions of liquid act as a multiplier of non-Darcy pressure drop, thereby reducing effective fracture conductivity (StimLab, 2002–2012) [courtesy StimLab].
Fig. 4. Transverse fractures in a horizontal well create flow convergence to a relatively small area causing high non-Darcy pressure loss (Shah et al., 2010).
small diameter wellbore, fluid velocities drastically increase in the near wellbore region exacerbating the non-Darcy and multiphase flow pressure losses in the fracture. Comparing a vertical well with a 100 foot tall single bi-wing planer fracture that is fully connected to the wellbore to the same 100 foot tall vertical fracture that is transversely connected to a 6 in. diameter horizontal wellbore, fluid velocity in the near wellbore is over 100 greater in the horizontal transverse case than in the vertical. Recall that in the Forchheimer Eq. (2), velocity is a squared term in the pressure drop calculation, resulting in a pressure drop of 16,000 times greater in the transverse horizontal case than the fully connected vertical well case. As a result, many design for higher conductivity in the near wellbore area by placing larger diameter, higher quality proppants and/or higher concentration. Although the proppant concentration specified in the baseline conductivity test is 2 lb/ft2, in most unconventional reservoir fracture treatments the proppant concentrations are typically much lower and often less than 1 lb/ft2. Lower proppant concentration yields a narrower propped fracture width which directly impacts conductivity (fracture perm fracture width) and indirectly affects conductivity due to the higher velocities required to flow through the narrower opening. Non-Darcy and multiphase flow effects are amplified by the narrower width. If a fracture width is halved, velocities are doubled and using Forchheimer's equation, non-Darcy pressure losses are increased by a factor of four. Width losses caused by frac gel filter cake and proppant embedment into the formation can further exacerbate this impact. The baseline conductivity of high quality proppants is often thousands of mD-ft yet under realistic conditions this conductivity often drops into the single digits. In addition to the conductivity reduction mechanisms discussed previously, such as non-Darcy and multiphase flow, transverse fractures and lower proppant concentrations, there are many others not presented here due to space limitations, such as embedment, fines migration, gel damage and the effect of elevated temperature on natural sands. Together they have a cumulative pressure loss effect that can reduce conductivity by orders of magnitude (Palisch et al., 2007). All proppants encounter a loss in effective conductivity due to these downhole conditions (Fig. 5), although the severity can vary depending on proppant type (sand, resin-coated sand or ceramic). Therefore, engineers must utilize fracture models equipped with
Fig. 5. Comparison between Baseline Conductivity and Realistic Conductivity at Eagle Ford Shale Conditions for a well in Webb County, TX. All proppant types lose significant conductivity, yet the Tier 1 proppant maintains nearly twice the conductivity as that of Tier 2 and three times that of Tier 3 (Bazan et al., 2012).
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algorithms designed to account for downhole conditions to design and optimize treatments, as well as select the appropriate proppant for each fracture design. 3.4. Cost-benefit analysis A critical component of the proppant selection process is the economic decision which balances the increased production yielded by increasing the fracture conductivity and the cost associated with that conductivity increase. This Cost-Benefit analysis can also be defined as the Economic Conductivity (EC) process – determining the conductivity required to maximize the economics of the well. The first step in the EC process is predicting an accurate conductivity at realistic conditions. This will typically lead an engineer to the conclusion that current fracture designs are conductivity-limited, meaning that any design modifications considered which would increase fracture conductivity should also result in additional production and EUR. Many design options are available which can effectively increase fracture conductivity such as the use of cleaner frac fluids, placing higher proppant concentrations, employing higher tier or quality proppants and utilizing larger mesh proppants. In most cases, all options should be considered when attempting to optimize fracture designs. Among the simplest of these options, however, is to focus on the proppant selection – proppant size and type. Nearly all proppant can be classified into one of three tiers of the proppant conductivity triangle (Fig. 6). The lowest tier, tier 3, consists of uncoated natural frac sand. Because this tier is the easiest to mine and the lowest cost to produce, it also makes up a majority of the current proppant market. Frac sands are currently employed in a broad range of applications due to their low cost and availability, but because they are naturally occurring they have irregular shape, exhibit a broad size distribution, are lower strength and therefore provide the lowest conductivity of the proppant tiers. Tier 2 consists of resin-coated sands which exhibit moderate conductivity. Resin can be placed on any sand and once cured (either prior to placement, or after stress is applied downhole) will improve the conductivity of the substrate. This is accomplished by encapsulating the fines generated when the sand grains crush and, in the case of curable resin coated sand, bonding together to create a higher strength proppant pack, while also preventing proppant flowback. Tier 1 includes the highest conductivity proppants on the market, consisting of man-made ceramics. When manufactured correctly these proppants are engineered to provide superior shape, tight size distributions, high strength and thermal stability – all of which are designed
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to provide superior conductivity. It should be reiterated that while nearly all proppant can be grouped into one of these three tiers, there can be significant quality differences within each tier depending on sand type (for Tiers 3 and 2), as well as raw material and manufacturing process in the case of Tier 1 ceramics. Since most hydraulic fractures are conductivity-limited, as outlined earlier, moving up the conductivity triangle will typically lead to increased production and EUR in most unconventional reservoirs. However, with this additional conductivity generally comes an increase in proppant cost, and therefore the second step in the EC process is to use a fracture model to determine the proppant and fracture design that maximizes the return on investment in the well. In many cases (as will be illustrated later) the proppant that provides the best economics may actually be a higher cost, higher conductivity proppant. The final step in the EC proppant selection process is to verify the modeling and recommendations by reviewing the impact of conductivity on actual production and the subsequent economics of the well. To summarize, proper proppant selection occurs through the following three-step Economic Conductivity process: (1) Utilize a frac model to estimate the fracture conductivity at realistic downhole conditions and predict the production performance achieved with various proppant types and/ or sizes, (2) Choose the proppant that maximizes economics of the well, (3) Execute the designs in the field and review actual production to validate the economic decision. 4. Field results validation Enormous amounts of literature exist that verify that increasing conductivity in most types of reservoirs throughout the world increases production (Vincent, 2002, 2009). The remainder of this article will review the field results in just two of these examples – the Eagle Ford and the Bakken. In the first example, actual production will be compared between direct offset wells to illustrate the economic conductivity process and illustrate economic benefits of increased conductivity in the Eagle Ford formation. In the second example, the use of Data and Artificial Neural Analysis will be used in the Bakken formation to determine the key completion parameters driving production enhancement, including proppant selection.
4.1. Eagle Ford Shale Spanning from central Texas south and into northeastern Mexico, the Eagle Ford Shale has experienced unprecedented growth over the last several years. The productive area spans over 20 Texas counties, and can be divided into three productive windows – dry gas, wet gas/condensate and oil. The depth ranges between 5000 and 14,000 ft, with thickness between 50 and 400 ft, porosity ranging from 4 to 11 percent and 50 to 1500 nanodarcy permeability (Pope et al., 2012). In the following example, the production performance between high conductivity tier 1 lightweight ceramic proppant and low conductivity tier 3 natural frac sand completion designs will be analyzed, focusing on two different well comparisons:
Fig. 6. The Proppant Conductivity Triangle of the three basic types of proppant which make up 99% of all proppant utilized in hydraulic fracturing. Conductivity, production and EUR typically improve as one moves up the triangle (Gallagher, 2011).
(1) Broad Area – All Eagle Ford Shale wells completed by a single operator in one County; (2) Offset Pair – Comparison of two offset wells completed at the same time with similar completion techniques with the intention of comparing a single variable (proppant).
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4.2. Broad area study This analysis represents a high-level production review of all wells drilled and completed by a single operator in Karnes County, Texas. The wells are located in the gas condensate window with TVD's ranging between 12,000 and 13,000 ft, averaging 4,000 ft lateral lengths, 3–5 million pounds of proppant and 14–18 stages per lateral. The operator initially focused on engineering fracture designs to maximize conductivity and production by selecting a lightweight ceramic (LWC) proppant. As experience in the area increased, the operator looked for ways to reduce well costs and elected to verify the selection of the higher cost lightweight ceramic, by completing several wells in the area with Tier 3 sand. In areas like the Eagle Ford, the fracture treatment typically makes up a significant portion of the entire drilling and completion (D&C) costs on a single well, with proppant being the single biggest line item. Therefore it was natural to look at proppant selection as an easy target for cost savings. In this case the operator saw a potential savings of over $1,000,000 to downgrade from Tier 1 LWC to a Tier 3 uncoated frac sand. Therefore a decision was made to pump sand in several nearby Eagle Ford wells and compare results. Many factors go into economic drivers of shale plays, for instance, one operator may decide it is better to save money on drilling and completion costs in order to deploy that capital towards additional wells, while others may deem maximizing each wells EUR as the most important factor. The production and economic analysis of the wells in this study will evaluate the overall economic impact of placing a more expensive high conductivity fracture. The LWC well group has 73 wells and the sand well group has 34 wells, all with at least 6 months production in the public domain. While there can be significant variation between individual wells in the Eagle Ford, both of these groups on average produce 50% of the BOE benefit from liquids, lending confidence that the two groups of wells have reasonably similar reservoir quality. In addition, the wells in both groups employed an average proppant volume of 3.7 million pounds per well, and had a fracture fluid volume of 80,000 bbls. Since the LWC wells had an average lateral length of 4600 ft while the sand wells have an average lateral length of slightly more at 4900 ft, the sand wells might carry a slight edge in completion potential. Predictive modeling suggested that the wells were conductivity limited, indicating that an increase in conductivity would yield an increase in production. Actual field results support this, as the LWC well group had a higher average cumulative 6 month BOE than the Sand wells (Fig. 7), with an incremental increase of 50,000 BOE per well in that time. Considering an oil price of $85/bbl and gas
Fig. 7. Cumulative production comparison between wells utilizing Tier 1 proppant (LWC) and those using Tier 3 proppant (Sand) during the first six months production for all of a single operators wells in Karnes Co., TX.
price of $3.50/mscf, the LWC wells on average generated an incremental $1.8 million per well in gross value from the additional production generated after just 6 months. Therefore, despite the sand wells potential to save an estimated $1 million in proppant costs, after just three months of production the investment in high a conductivity fracture had “paid-out” the additional cost. Moreover, after six months the high conductivity wells had generated an additional $700,000 per well. To further strengthen the analysis, the wells in this broad group were also grouped into three smaller areas, based on proximity. One area had a significant population of both LWC and Sand wells, all within a 5 mile radius. This group was analyzed and found to exhibit similar conclusions as were found in the broader area, with the LWC wells generating a substantial improvement in both raw production and value. Specifically, the LWC wells produced an average of 158,000 BOE per well after six months which is 57,000 BOE more than the 101,000 BOE that the sand wells produced, and resulted in nearly $3.5 million in incremental value generated per well.
4.3. Offset pair study In this Eagle Ford study two oil wells that are directly offset to each other in Karnes County from another operator were analyzed. Both wells were drilled, completed and produced in a similar manner to create an “apples-to-apples” comparison. They are actually the first two wells that are part of a larger ongoing study in which a 10 well trial was designed to compare the impact of conductivity across several areas. Both wells have 18 stages, 6000 ft toe-up laterals and 11,900 ft TVD to mid-perforation. Post frac production of the two wells were pressure managed in the same manner by changing choke sizes at the same time, until the flowing pressure began to decline rapidly in the low conductivity well (around 100 days into production). Each well had similar fracture designs except for proppant selection and proppant volume. The high conductivity well utilized 1.3 million pounds (mmlbs) 30/50 sand with a tail-in of 3.4 mmlbs 20/40 LWC for a total of 4.7 mmlbs of proppant in the well. The low conductivity well primarily utilized 20/40 sand (4.7 mmlbs) along with a tail-in of 0.7 mmlbs of 20/40 curable resin coated sand for a total proppant volume of 5.7 mmlbs. Despite the sand well receiving an additional 0.7 mmlbs of proppant, the LWC well still had an incremental cost of $500,000. Fig. 8 depicts the production profile of the high conductivity LWC well (red) vs the low conductivity sand well (green), while they were produced with the same pressure management scheme.
Fig. 8. Offset Eagle Ford Shale wells illustrating the value of increasing fracture conductivity. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)
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The Bakken formation is an oil play that extends more than 200,000 square miles throughout North Dakota, Montana, Saskatchewan and Manitoba. It is a Mississippian/Devonian age formation with a majority of the activity targeting the Middle Bakken and Three Forks formations, although there are other intervals that are productive (Rankin et al., 2010). The Middle Bakken is a dolomite sequence containing interbedded siltstones and sandstones and is considered to be a tight formation, with permeabilities ranging between 0.1 and 0.01 mD. While the Bakken has been under varying degrees of development for many years, the introduction of horizontal drilling with multiple transverse fractures has led to a dramatic increase in activity in the play in recent years. In this example, a data-driven artificial neural network model was used to identify important parameters in the study wells, leading engineers to optimize on completion variables that have the biggest influence on production (Shelly et al., 2012). During the development of the data driven Bakken model, engineers selected 40 wells obtaining data commonly captured in mud log, completion, frac and production reports (Fig. 9). These wells were spread out across five North Dakota counties and were of varying lateral lengths, fracture stages, fracture fluid types, proppant types and volumes. As is typically the case in most unconventional developments, openhole logs were not run in these horizontal wells so parameters typically used to classify reservoir potential – such as porosity, water saturation, and permeability – are simply not available. However, data that is commonly available in mud reports, such as gas shows, gas fractions and mud weights, along with such parameters as formation top and formation thickness, have been identified as excellent proxies for reservoir quality. The data driven model uses a feed forward artificial neural network (ANN). The Bakken model was originally trained using 30 of the dataset wells, holding the remaining 10 wells out for model validation purposes. The ANN is trained by back-propagating variables to match two known production outcomes, in this case “Peak Oil Month” and “Oil Recovery”. Many tens of thousands of model variations are executed in developing the model that best
predicts production based on the readily available data. The data is reduced down to only the most important variables, referred to as predictors, which is typically in the neighborhood of 10–15 parameters. The single model selected as the best fit is then validated with the 10 wells that were held out of the training. Once the Bakken data driven model is selected and validated, sensitivities can be performed to determine the impact of each predictor on the two production outcomes. The predictors consist of several controllable parameters related to completion and fracture treatments, as well as non-controllable parameters that are reservoir quality driven (Fig. 10). In the case of the Bakken model, three of the top five most impactful parameters are noncontrollable – butane value, methane value and total gas volumes in the mud log. All three have strong correlations to high oil production. It is no surprise that reservoir quality is a primary driver of well performance. However, there are also many predictors that have a good correlation to strong oil production which are controllable. The number of fracture treatments, proppant (conductivity), perforation and staging lengths all have strong correlations to production. Once trained, the Bakken data-driven model adds value by being able to quickly optimize the completion design using data readily available during the drilling process, and subsequently running multiple sensitivities to the completion and fracture parameters prior to final completion design. In this example, a 9400 ft horizontal lateral was drilled, with total mud log gas averaging 2400 units and C1/C4 gas ratio of 3, indicating the wellbore was located in high quality reservoir rock. The operator's initial design (prior to running the Bakken ANN model) called for 40 fracture stages utilizing a total of 5,300,000 lb of Tier 1 20/40 ceramic proppant placed with approximately 95,000 bbls crosslinked fluid and 9500 bbls linear gel for a total cost of $6,600,000. The Bakken ANN model was then used to “optimize” the completion. Results of the model concluded that greater Net Present Value could be obtained by reducing treatment volume and the number of stages from 40 to 30. These changes were predicted to result in modest production impacts, yet when considered along with the cost savings, yielded the greatest net present value (Fig. 11). These recommendations were then applied to the actual completion design and the production achieved a best month rate of approximatly 36,000 bbls oil, resulting in one of the best wells in the area. Another application of the model is to take a well that has already been completed and placed on production, and use the model to run sensitivities to alternative completion options to determine the value of the decisions. For example, in another well, the model was initially run to ensure it matched actual production. Then, sensitivities were run first to identify the value of utilizing the LWC over white Sand, and then to identify the optimal
Fig. 9. Summary of data for Bakken data-driven model development (Shelly et al., 2012).
Fig. 10. Impact of Bakken controllable and non-controllable model parameters (Shelly et al., 2012).
After 100 days of similar choke management, the LWC well produced 15% more oil and 11% more gas, resulting in an overall 14% increase in BOE. Once again, assuming $85/bbl oil and $3.50/ mscf gas, and using actual production, after 100 days the LWC and sand wells generated gross values of $5.7 and $5.0 million, respectively. Considering the additional $500,000 investment to provide the increased conductivity the LWC well pays out the gross investment in 45 days and generates an incremental $200,000 in value over the Sand well after just 100 days. 4.4. Bakken
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completion strategy (Fig. 12). In this well, by using 100% LWC an additional 50,000 BOE were produced, creating an additional $1.5 million in NPV and improving the ROI by nearly 20%. Also, it was projected that if the stage count had been increased to 30, crosslinked fluids employed and 100% of the proppant converted to 20/40 LWC, the well could have produced an additional 200,000 BOE more, created nearly $6 million in incremental NPV and increased the ROI by 35%.
Proppant selection is driven by several parameters:
5. Summary
Conductivity is the product of proppant pack permeability and fracture width.
Reference Conductivity is measured in the lab using the ISO/API Conductivity Test Procedures.
While useful for gross comparisons, ISO/API Reference Conductivity results do not represent the realistic conductivity of the fracture at downhole conditions.
Fig. 11. Evaluation of completion parameters for optimized net present value (Shelly et al., 2012).
○ Proppant availability can drive selection choices in periods of high demand. In addition, high demand may promote an influx of inferior proppant quality within all three proppant tiers. ○ Fluid selection can drive proppant selection – when using low viscosity fluids, smaller proppant sizes will be required due to poor proppant transport and narrow pumping widths, while high viscosity fluids create wider fracs and can transport larger sized proppant, allowing for more conductivity to be placed. ○ Conductivity requirements typically drive proppant selection, and are impacted by downhole flowing conditions which must be accounted for with sophisticated fracture models. In unconventional reservoirs, these include Non-Darcy and multiphase flow, flow convergence of transverse fractures near the wellbore, low proppant concentrations, higher temperatures and embedment to name a few. Despite their low permeability, in most unconventional reservoirs hydraulic fractures are conductivity limited, meaning that an increase in conductivity will yield an increase in production. ○ Proppant selection is ultimately driven by a cost-benefit analysis, called Economic Conductivity, which should be used to select the proppant that provides the optimal combination of conductivity benefits (increased production and EUR) and cost. For many, proppant selection in today's unconventional reservoirs is often driven by upfront cost saving opportunities. However, investments in conductivity have been demonstrated to payout early in the production life of the well and provide incremental value which will improve the production, EUR and the ultimate economics of the well. Data Driven modeling in the Bakken and other formations can be applied to quickly optimize completion and fracture treatment execution by utilizing data that is easily obtainable in horizontal well drilling, and to maximize the net present value of the well.
Fig. 12. Modeling of an existing Bakken well completion/performance. Incremental value and higher ROI realized by using higher conductivity proppant in original design. In addition, significant upside potential was observed when increased stages, crosslinked fluid and higher conductivity were employed.
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References API-RP-19D, 2008. Recommended Practice for Measuring the Long-term Conductivity of Proppants, First Edition, May 2008. API Publishing Services, 1220 L Street, N.W., Washington, D.C. 20005. Bazan, L.W., et al, 2012. Hydraulic Fracture Design and Well Production Results in the Eagle Ford Shale: One Operator's Perspective. Paper SPE-155779, Presented at the SPE Americas Unconventional Resources Conference, Pittsburgh, PA, 5–7 June. Forchheimer, P., 1901. Wasserbewegung durch Boden. ZVDI 45, 1781. Gallagher, D., 2011. The hierarchy of oily conductivity. Guest Editorial. J. Pet. Technol. 63 (4). International Organization for Standardization, 2006. Procedures for Measuring the Long-Term Conductivity of Proppants. ISO 13503-5, July, 2006. Palisch, T., et al, 2007. Determining Realistic Fracture Conductivity and Understanding its Impact on Well Performance – Theory and Field Examples. Paper SPE-10630, Presented at the SPE Hydraulic Fracturing Technology Conference, College Station, Texas, 29–31 January. Palisch, T., et al, 2012. Hydraulic Fracture Design Optimization in Unconventional Reservoirs – A Case History. Paper SPE-160206, Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, 8–10 October. Pope, C., et al, 2012. Improving Completion and Stimulation Effectiveness in Unconventional Reservoirs – Field Results in the Eagle Ford Shale of North America. Paper SPE-152839, Presented at the SPE SPE/EAGE European Unconventional Resources Conference and Exhibition, Vienna, Austria, 20–22 March.
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Rankin, R., et al, 2010. Improved Production and Profitability Achieved With Superior Completions in Horizontal Wells: A Bakken/Three Forks Case History. Paper SPE-134595 Presented at the SPE Annual Technical Conference and Exhibition, Florence, Italy, 19–22 September. Shah, S., et al., 2010. Fracture Orientation and Proppant Selection for Optimizing Production in Horizontal Wells. Paper SPE-128612 Presented at the SPE Oil and Gas India Conference and Exhibition, Mumbai, India, 20–22 January. Shelly, R., et al., 2012. A Novel Method to Optimize Horizontal Bakken Completions in a Factory Mode Development Program. Paper SPE-159696 Presented at the SPE Annual Technical Conference and Exibitions, San Antonio, Texas, USA, 8–10 October. StimLab, 2002–2012. Proppant Consortium Notes from 2002–2012. Vincent, M.C., 2002. Proving It – A Review of 80 Published Field Studies Demonstrating the Importance of Increased Fracture Conductivity. Paper SPE77675 Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, TX, 29 September–2 October. Vincent, M.C., 2009. Examining Our Assumptions – Have Oversimplifications Jeopardized Our Ability to Design Optimal Fracture Treatments? Paper SPE119143 Presented at the SPE Hydraulic Fracturing Conference, The Woodlands, TX, 19–21 January. Vincent, M.C., 2011. Optimizing Transverse Fractures in Liquid-Rich Formations. Paper SPE-146376 presented at the SPE Annual Technical Conference and Exhibition, Denver, CO, 31 October–2 November.
Contents lists available at ScienceDirect
Journal of Petroleum Science and Engineering journal homepage: www.elsevier.com/locate/petrol
Fracture conductivity – Design considerations and benefits in unconventional reservoirs Mark Chapman, Terry Palisch n CARBO Ceramics Inc, USA
art ic l e i nf o
a b s t r a c t
Article history: Received 21 August 2013 Received in revised form 10 September 2014 Accepted 13 September 2014 Available online 28 September 2014
Hydraulic fracturing is critical to the success of the petroleum industry in the twenty-first century, turning previously uneconomic reservoirs into success stories through the application of horizontal wellbores and multiple transverse fracture treatments. The ability to contact large volumes of rock in these ultra-tight reservoirs through a single horizontal wellbore has allowed for reduced cost of drilling when compared to multiple vertical wells. While most engineers tend to understand the wellbore and its components, the hydraulic fracture treatment is typically less understood. However, the wellbore and the hydraulic fractures are expected to provide a conductive flow path to the reservoir for the life of the well, so the comprehension of each are critical to the long term viability of the completion. Of course when it comes to the hydraulic fracture, the proppant is the material that is expected to provide this long term connectivity (a.k.a. conductivity), so its selection should be treated the same as the selection of wellbore components – it should be chosen by looking at all phases of the well's production, including the service when the well is first placed on production, the environment if/when the well is placed on artificial lift, and the conditions when the well is producing 20 years later. This paper will present a thorough review of fracture conductivity principles, with particular emphasis on the impact that downhole conditions has on its realistic measure. After reviewing the primary parameters that will impact fracture conductivity, the authors will review the main components of proppant selection, specifically as it relates to the process in unconventional reservoirs over the last several years, and will present a methodology which will lead a design engineer to the optimal fracture design. Finally, case histories which illustrate the application of these theories will be presented for actual completions in both the Bakken and Eagle Ford plays. This paper will be beneficial to those engineers and technologists who are interested in designing optimal completions in their wells and maximizing the return on their investment. & 2014 Elsevier B.V. All rights reserved.
Keywords: hydraulic fracturing completion design proppant selection case histories
1. Introduction Hydraulic fractures have historically been placed for one of two reasons; to accelerate production or to increase the recovery. In many “conventional” reservoirs with sufficient permeability to flow on their own, fracture treatments are designed to accelerate the production rate and thereby improve the present value of the well and reserves. Conversely, in today's unconventional reservoirs, engineers are primarily placing fracture treatments to drain formations that, due to their extremely low permeability, cannot economically produce hydrocarbons from a single, unstimulated
n
Corresponding author. E-mail addresses: [email protected] (M. Chapman), [email protected] (T. Palisch). http://dx.doi.org/10.1016/j.petrol.2014.09.015 0920-4105/& 2014 Elsevier B.V. All rights reserved.
vertical wellbore. By employing horizontal wells and multiple fracture treatments, these wells become economic and add significant reserves. Adding reserves in unconventional plays is dependent on increasing reservoir contact and enhancing flow capacity (conductivity). Reservoir contact is affected by lateral length, stage count, fracture height and fracture half-length. Engineers are able to control fracture height and half-length with fluid rate, volume and viscosity. However, reservoir contact is not beneficial on its own, as sustained flow capacity (or fracture conductivity) must be provided in these fractures so that the link between the reservoir contact and the wellbore remains viable long after the frac pumps and equipment have left the wellsite. Sustained fracture conductivity is primarily governed by proppant volume and selection. Characteristics of proppant that affect conductivity include proppant size, proppant type/quality, proppant concentration/mass (fracture width), proppant durability, fluid clean
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MSCFD ml/min ΔPfrac
Nomenclature API
β
BO BOE KCl kfrac lb/ft2 mD
American Petroleum Institute Proppant beta factor Barrels of Oil Barrels of Oil Equivalent Potassium chloride Fracture permeability Pounds per square foot MilliDarcies
ρfluid TVD
μfluid
vfluid wfrac Xfrac
up, embedment/spalling, and fines migration. The remainder of this paper will discuss these characteristics, their impact on fracture conductivity and ultimately their impact on production rate, EUR and the economics of the well.
2. Conductivity In unconventional/resource plays, millions of dollars are typically required to drill and complete the horizontal wellbores, install isolation systems, place surface production equipment and pump hydraulic fracture treatments in multiple stages. The fracture conductivity placed via the hydraulic fracture treatment is critical since without a sufficiently conductive pathway for hydrocarbon to flow through the fracture to the wellbore, the rest of the aforementioned equipment is either wasted or suboptimal. Fracture conductivity (Cf) is a function of the permeability of the fracture and the fracture width, and is represented by the following equation: C f ¼ kf rac wf rac
ð1Þ
Conductivity is most commonly reported as the baseline or reference conductivity for each proppant size and type. Baseline conductivity is obtained through the “long-term” conductivity test as set forth by the International Organization for Standardization (ISO), under ISO-13503-5. In 2008, the American Petroleum Institute also adopted this standard under API-RP-19D. The ISO/ API conductivity procedures are designed to give a reasonably repeatable determination of the flow capacity of the proppant pack under stress. Some significant details of the test are: (1) utilizes the Cooke Conductivity Cell (Fig. 1); (2) proppant is loaded into the conductivity cell at 2 lb/ft2; (3) proppant is placed
Thousand standard cubic feet per day Milliliters per minute Pressure drop in the fracture Fluid density True vertical depth Fluid viscosity Fluid velocity Fracture width Fracture half length
between 5 million psi Young's Modulus Ohio Sandstone shims; (4) the test is performed at 150 1F for sand and 250 1F for ceramic proppant; (5) stress is increased at a uniform rate and then held for 50 h at the target stress prior to testing; (6) 2% KCl fluid is circulated at an extremely low rate of 2 ml/min and (7) Darcy's Law is used to calculate the permeability and conductivity of the proppant pack. The ISO/API test is designed to allow impartial comparison between proppants under controlled conditions to generate repeatable results. However, API-RP-19D states that results from long term conductivity testing are “not intended for use in obtaining absolute values of proppant pack conductivities under downhole reservoir conditions” (API-RP-19D, 2008). Although this test does not reflect all downhole conditions, it does holistically account for proppant size and strength (crush), it measures pressure drop of a fluid flowing through the pack, and does account for some temperature and embedment reductions. However, since the test doesn't account for many conditions that exist downhole such as non-Darcy and multiphase flow, reduced proppant concentration and flow convergence to name a few, additional tools and methodology are required to accurately predict realistic conductivity, and properly select the optimal proppant for a given completion.
3. Proppant selection The proppant in the fracture is the critical link connecting the hydrocarbons in the reservoir to the wellbore and sales meter. Unfortunately proppant selection is often simplified to single parameters such as depth, stress and crush, or the notion that in extremely tight rock any proppant is good enough. In some cases, proppant selection is merely limited to what the last engineer did, or perhaps what the operator next door is using. However, as engineers we have the ability and perhaps even the obligation to do better. In reality, proppant selection requires the evaluation of multiple factors to optimize for flow performance and economics. Four factors should be considered when selecting proppant in unconventional reservoirs – proppant availability, fracture fluid selection, conductivity requirements and cost-benefit analysis. 3.1. Proppant availability
Fig. 1. Cooke Conductivity Cell used in the measurement of baseline conductivity per ISO-13503-5 (International Organization for Standardization, 2006).
As the development of unconventional reservoirs gained momentum in the mid-2000s, proppant demand increased dramatically, with annual worldwide usage increasing from 5 billion pounds in 2004 to 60–70 billion pounds in 2011, or a 12– 14 fold increase (Palisch et al., 2012). This increased demand had a profound impact on proppant selection, and in many cases led to simply a “bring whatever is available” mentality. To absorb this additional demand, new sand mines of varying quality were quickly opened, new resin coated sand facilities were quickly
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reservoirs require more fracture conductivity than a gas well, leading engineers to select larger mesh proppants such as 20/40 and 30/50. This has led to a subsequent evolution of frac fluid systems in unconventional liquids-rich reservoirs, namely the hybrid fluid design. A hybrid fluid design involves using a low viscosity fluid in the pad and early proppant stages and then as proppant concentrations are increased and/or larger mesh proppants are employed, a higher viscosity fluid (and XL) is used. These fluids are necessary to place larger diameter proppants. 3.3. Conductivity requirements
Fig. 2. Cross-section comparison between various poorly manufactured ceramic proppants, and a high quality ceramic proppant (indicated by red star). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
commissioned, and new ceramic proppant plants were constructed or other industry plants were reconfigured for the manufacture of ceramic proppant. In addition to a shortage of higher tier proppants, this extreme demand led to an influx of large volumes of lower quality proppant in all proppant tiers. Sieve distribution, shape and strength are often sacrificed with sand from inferior mines/ deposits. Premium resin coated sand also requires premium white sand as the substrate, and competes for the same high quality sand deposits. When premium sand is unavailable, then lower quality sands are coated. Ceramic plants require more capital and experience, and take longer to build and develop the expertise to consistently manufacture a high quality ceramic proppant. Ceramic proppant quality is dependent on raw materials, process control and proper firing methods. If high standards of quality control are not held, pellets will develop characteristics that harm conductivity (Fig. 2) such as; internal porosity (lower strength), rough surfaces and, irregular shapes. 3.2. Frac fluid selection With the initial development of the Barnett Shale, fracturing fluid selection moved away from traditional high viscosity crosslinked (XL) gel systems to low viscosity slickwater (SW) or linear gel (LG) systems. High viscosity XL systems are used for many reasons, including improved proppant transport, fracture width generation and planar fracture development. Low viscosity SW/LG systems are used when complex fracture systems are desired, as well as to minimize fluid damage and in some cases reduce costs. However, SW/LG fluids also require that smaller proppants (e.g. 40/70 and 100-mesh) be employed since these smaller diameter proppants are easier to place in the narrower fractures, as well as transport in the fracture. In reservoirs such as the Barnett Shale, where the rock contains an existing cross-cutting fracture system and relatively isotropic horizontal stresses, the development of a complex fracture network is desirable, and in many cases offsets the lower conductivity fractures they create. Complexity creates more reservoir contact, which can be beneficial in many shale gas reservoirs in which hydrocarbons cannot flow readily through the rock on their own. In 2011, shale gas development slowed due to an extended period of depressed natural gas prices and with oil prices in excess of $100/BO, focus shifted to liquid rich resource plays such as the Bakken, Eagle Ford and Utica Shale. Due to the higher viscosity producing fluids and multiphase flow, wells in oil and condensate
Fracture conductivity provides the vital link between the contacted reservoir and the wellbore, and the amount of conductivity required to optimally drain the reservoir is dictated by the deliverability of the rock. Reservoir deliverability is driven by two parameters – reservoir contact and reservoir permeability. While the required fracture conductivity is proportional to the reservoir deliverability, and in many cases the magnitude of the reservoir contact and/or permeability are difficult to determine, the focus of this section the impact of determining an accurate estimate of fracture conductivity. As discussed earlier, realistic fracture conductivity differs from baseline conductivity in that it includes downhole conditions such as non-Darcy and multi-phase flow, lower proppant concentrations, flow convergence in transverse fractures, etc. Often referred to as “realistic conductivity”, the conductivity at realistic conditions is typically found to be orders of magnitude lower than baseline conductivity (Palisch et al., 2007). Each proppant type and size is affected differently by these conditions, and typically requires the use of sophisticated fracture models to determine a proper estimate of realistic conductivity and subsequent proppant selection. The ISO/API baseline conductivity test is run at Darcy conditions (2 ml/min) which are not representative of realistic flowing conditions. If a well were to produce at baseline conditions, it would be equivalent to 6 BPD in a fully perforated vertical oil well with a 50 ft tall bi-wing frac achieving 2 lb/ft2 concentration, or 15 MSCFD flowing at 1500 psi and 250 1F in a similar dry gas well. Increasing velocities in the fracture to more realistic production rates results in additional pressure losses that are not addressed in Darcy's Law. Forchheimer's Equation (Eq. (2)) modifies Darcy's equation to account for both the Darcy pressure losses (mv/k), which are equally controlled by fluid viscosity and velocity and fracture permeability, and the non-Darcy pressure losses (βρv2) which are dominated by the exponential velocity term (Forchheimer, 1901).
ΔP f rac X f rac
¼
μf luid νf luid kf rac
þ βρf luid ν2 f luid
ð2Þ
The baseline conductivity test also utilizes a single-phase 2% KCl fluid. Unfortunately, single phase flow rarely exists in actual flowing fracture conditions. Actual wells consist of a combination of oil, gas and water, all of which compete for space to flow, causing a complex flow regime that leads to much higher pressure drop in the fracture than what is measured in the baseline test. This multiphase flow is typically demonstrated as a non-Darcy effect multiplier due to its nature of having greater affect as velocities increase (Fig. 3). Lab data by various researchers confirm that pressure losses increase even when small percentage of a second phase is included. One of the well completion goals in most unconventional reservoirs is to place numerous transverse bi-wing hydraulic fractures within one single horizontal wellbore, thereby increasing the reservoir contact of a single wellbore by orders of magnitude over a fractured vertical well (Vincent, 2011). However, wells completed with multiple transverse hydraulic fractures create
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another challenge. Fluids flowing through these massive transverse fractures must all eventually converge on the single, relatively small diameter wellbore (Fig. 4). This can lead to significant pressure drop in the near-wellbore region of the fracture. Unlike conventional vertical wells in which the fluids in vertical bi-wing fractures can generally flow directly into the wellbore, the fluids in horizontal transverse fractures must eventually flow radially into the wellbore. As fluids converge on the relatively
Fig. 3. Small fractions of liquid act as a multiplier of non-Darcy pressure drop, thereby reducing effective fracture conductivity (StimLab, 2002–2012) [courtesy StimLab].
Fig. 4. Transverse fractures in a horizontal well create flow convergence to a relatively small area causing high non-Darcy pressure loss (Shah et al., 2010).
small diameter wellbore, fluid velocities drastically increase in the near wellbore region exacerbating the non-Darcy and multiphase flow pressure losses in the fracture. Comparing a vertical well with a 100 foot tall single bi-wing planer fracture that is fully connected to the wellbore to the same 100 foot tall vertical fracture that is transversely connected to a 6 in. diameter horizontal wellbore, fluid velocity in the near wellbore is over 100 greater in the horizontal transverse case than in the vertical. Recall that in the Forchheimer Eq. (2), velocity is a squared term in the pressure drop calculation, resulting in a pressure drop of 16,000 times greater in the transverse horizontal case than the fully connected vertical well case. As a result, many design for higher conductivity in the near wellbore area by placing larger diameter, higher quality proppants and/or higher concentration. Although the proppant concentration specified in the baseline conductivity test is 2 lb/ft2, in most unconventional reservoir fracture treatments the proppant concentrations are typically much lower and often less than 1 lb/ft2. Lower proppant concentration yields a narrower propped fracture width which directly impacts conductivity (fracture perm fracture width) and indirectly affects conductivity due to the higher velocities required to flow through the narrower opening. Non-Darcy and multiphase flow effects are amplified by the narrower width. If a fracture width is halved, velocities are doubled and using Forchheimer's equation, non-Darcy pressure losses are increased by a factor of four. Width losses caused by frac gel filter cake and proppant embedment into the formation can further exacerbate this impact. The baseline conductivity of high quality proppants is often thousands of mD-ft yet under realistic conditions this conductivity often drops into the single digits. In addition to the conductivity reduction mechanisms discussed previously, such as non-Darcy and multiphase flow, transverse fractures and lower proppant concentrations, there are many others not presented here due to space limitations, such as embedment, fines migration, gel damage and the effect of elevated temperature on natural sands. Together they have a cumulative pressure loss effect that can reduce conductivity by orders of magnitude (Palisch et al., 2007). All proppants encounter a loss in effective conductivity due to these downhole conditions (Fig. 5), although the severity can vary depending on proppant type (sand, resin-coated sand or ceramic). Therefore, engineers must utilize fracture models equipped with
Fig. 5. Comparison between Baseline Conductivity and Realistic Conductivity at Eagle Ford Shale Conditions for a well in Webb County, TX. All proppant types lose significant conductivity, yet the Tier 1 proppant maintains nearly twice the conductivity as that of Tier 2 and three times that of Tier 3 (Bazan et al., 2012).
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algorithms designed to account for downhole conditions to design and optimize treatments, as well as select the appropriate proppant for each fracture design. 3.4. Cost-benefit analysis A critical component of the proppant selection process is the economic decision which balances the increased production yielded by increasing the fracture conductivity and the cost associated with that conductivity increase. This Cost-Benefit analysis can also be defined as the Economic Conductivity (EC) process – determining the conductivity required to maximize the economics of the well. The first step in the EC process is predicting an accurate conductivity at realistic conditions. This will typically lead an engineer to the conclusion that current fracture designs are conductivity-limited, meaning that any design modifications considered which would increase fracture conductivity should also result in additional production and EUR. Many design options are available which can effectively increase fracture conductivity such as the use of cleaner frac fluids, placing higher proppant concentrations, employing higher tier or quality proppants and utilizing larger mesh proppants. In most cases, all options should be considered when attempting to optimize fracture designs. Among the simplest of these options, however, is to focus on the proppant selection – proppant size and type. Nearly all proppant can be classified into one of three tiers of the proppant conductivity triangle (Fig. 6). The lowest tier, tier 3, consists of uncoated natural frac sand. Because this tier is the easiest to mine and the lowest cost to produce, it also makes up a majority of the current proppant market. Frac sands are currently employed in a broad range of applications due to their low cost and availability, but because they are naturally occurring they have irregular shape, exhibit a broad size distribution, are lower strength and therefore provide the lowest conductivity of the proppant tiers. Tier 2 consists of resin-coated sands which exhibit moderate conductivity. Resin can be placed on any sand and once cured (either prior to placement, or after stress is applied downhole) will improve the conductivity of the substrate. This is accomplished by encapsulating the fines generated when the sand grains crush and, in the case of curable resin coated sand, bonding together to create a higher strength proppant pack, while also preventing proppant flowback. Tier 1 includes the highest conductivity proppants on the market, consisting of man-made ceramics. When manufactured correctly these proppants are engineered to provide superior shape, tight size distributions, high strength and thermal stability – all of which are designed
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to provide superior conductivity. It should be reiterated that while nearly all proppant can be grouped into one of these three tiers, there can be significant quality differences within each tier depending on sand type (for Tiers 3 and 2), as well as raw material and manufacturing process in the case of Tier 1 ceramics. Since most hydraulic fractures are conductivity-limited, as outlined earlier, moving up the conductivity triangle will typically lead to increased production and EUR in most unconventional reservoirs. However, with this additional conductivity generally comes an increase in proppant cost, and therefore the second step in the EC process is to use a fracture model to determine the proppant and fracture design that maximizes the return on investment in the well. In many cases (as will be illustrated later) the proppant that provides the best economics may actually be a higher cost, higher conductivity proppant. The final step in the EC proppant selection process is to verify the modeling and recommendations by reviewing the impact of conductivity on actual production and the subsequent economics of the well. To summarize, proper proppant selection occurs through the following three-step Economic Conductivity process: (1) Utilize a frac model to estimate the fracture conductivity at realistic downhole conditions and predict the production performance achieved with various proppant types and/ or sizes, (2) Choose the proppant that maximizes economics of the well, (3) Execute the designs in the field and review actual production to validate the economic decision. 4. Field results validation Enormous amounts of literature exist that verify that increasing conductivity in most types of reservoirs throughout the world increases production (Vincent, 2002, 2009). The remainder of this article will review the field results in just two of these examples – the Eagle Ford and the Bakken. In the first example, actual production will be compared between direct offset wells to illustrate the economic conductivity process and illustrate economic benefits of increased conductivity in the Eagle Ford formation. In the second example, the use of Data and Artificial Neural Analysis will be used in the Bakken formation to determine the key completion parameters driving production enhancement, including proppant selection.
4.1. Eagle Ford Shale Spanning from central Texas south and into northeastern Mexico, the Eagle Ford Shale has experienced unprecedented growth over the last several years. The productive area spans over 20 Texas counties, and can be divided into three productive windows – dry gas, wet gas/condensate and oil. The depth ranges between 5000 and 14,000 ft, with thickness between 50 and 400 ft, porosity ranging from 4 to 11 percent and 50 to 1500 nanodarcy permeability (Pope et al., 2012). In the following example, the production performance between high conductivity tier 1 lightweight ceramic proppant and low conductivity tier 3 natural frac sand completion designs will be analyzed, focusing on two different well comparisons:
Fig. 6. The Proppant Conductivity Triangle of the three basic types of proppant which make up 99% of all proppant utilized in hydraulic fracturing. Conductivity, production and EUR typically improve as one moves up the triangle (Gallagher, 2011).
(1) Broad Area – All Eagle Ford Shale wells completed by a single operator in one County; (2) Offset Pair – Comparison of two offset wells completed at the same time with similar completion techniques with the intention of comparing a single variable (proppant).
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4.2. Broad area study This analysis represents a high-level production review of all wells drilled and completed by a single operator in Karnes County, Texas. The wells are located in the gas condensate window with TVD's ranging between 12,000 and 13,000 ft, averaging 4,000 ft lateral lengths, 3–5 million pounds of proppant and 14–18 stages per lateral. The operator initially focused on engineering fracture designs to maximize conductivity and production by selecting a lightweight ceramic (LWC) proppant. As experience in the area increased, the operator looked for ways to reduce well costs and elected to verify the selection of the higher cost lightweight ceramic, by completing several wells in the area with Tier 3 sand. In areas like the Eagle Ford, the fracture treatment typically makes up a significant portion of the entire drilling and completion (D&C) costs on a single well, with proppant being the single biggest line item. Therefore it was natural to look at proppant selection as an easy target for cost savings. In this case the operator saw a potential savings of over $1,000,000 to downgrade from Tier 1 LWC to a Tier 3 uncoated frac sand. Therefore a decision was made to pump sand in several nearby Eagle Ford wells and compare results. Many factors go into economic drivers of shale plays, for instance, one operator may decide it is better to save money on drilling and completion costs in order to deploy that capital towards additional wells, while others may deem maximizing each wells EUR as the most important factor. The production and economic analysis of the wells in this study will evaluate the overall economic impact of placing a more expensive high conductivity fracture. The LWC well group has 73 wells and the sand well group has 34 wells, all with at least 6 months production in the public domain. While there can be significant variation between individual wells in the Eagle Ford, both of these groups on average produce 50% of the BOE benefit from liquids, lending confidence that the two groups of wells have reasonably similar reservoir quality. In addition, the wells in both groups employed an average proppant volume of 3.7 million pounds per well, and had a fracture fluid volume of 80,000 bbls. Since the LWC wells had an average lateral length of 4600 ft while the sand wells have an average lateral length of slightly more at 4900 ft, the sand wells might carry a slight edge in completion potential. Predictive modeling suggested that the wells were conductivity limited, indicating that an increase in conductivity would yield an increase in production. Actual field results support this, as the LWC well group had a higher average cumulative 6 month BOE than the Sand wells (Fig. 7), with an incremental increase of 50,000 BOE per well in that time. Considering an oil price of $85/bbl and gas
Fig. 7. Cumulative production comparison between wells utilizing Tier 1 proppant (LWC) and those using Tier 3 proppant (Sand) during the first six months production for all of a single operators wells in Karnes Co., TX.
price of $3.50/mscf, the LWC wells on average generated an incremental $1.8 million per well in gross value from the additional production generated after just 6 months. Therefore, despite the sand wells potential to save an estimated $1 million in proppant costs, after just three months of production the investment in high a conductivity fracture had “paid-out” the additional cost. Moreover, after six months the high conductivity wells had generated an additional $700,000 per well. To further strengthen the analysis, the wells in this broad group were also grouped into three smaller areas, based on proximity. One area had a significant population of both LWC and Sand wells, all within a 5 mile radius. This group was analyzed and found to exhibit similar conclusions as were found in the broader area, with the LWC wells generating a substantial improvement in both raw production and value. Specifically, the LWC wells produced an average of 158,000 BOE per well after six months which is 57,000 BOE more than the 101,000 BOE that the sand wells produced, and resulted in nearly $3.5 million in incremental value generated per well.
4.3. Offset pair study In this Eagle Ford study two oil wells that are directly offset to each other in Karnes County from another operator were analyzed. Both wells were drilled, completed and produced in a similar manner to create an “apples-to-apples” comparison. They are actually the first two wells that are part of a larger ongoing study in which a 10 well trial was designed to compare the impact of conductivity across several areas. Both wells have 18 stages, 6000 ft toe-up laterals and 11,900 ft TVD to mid-perforation. Post frac production of the two wells were pressure managed in the same manner by changing choke sizes at the same time, until the flowing pressure began to decline rapidly in the low conductivity well (around 100 days into production). Each well had similar fracture designs except for proppant selection and proppant volume. The high conductivity well utilized 1.3 million pounds (mmlbs) 30/50 sand with a tail-in of 3.4 mmlbs 20/40 LWC for a total of 4.7 mmlbs of proppant in the well. The low conductivity well primarily utilized 20/40 sand (4.7 mmlbs) along with a tail-in of 0.7 mmlbs of 20/40 curable resin coated sand for a total proppant volume of 5.7 mmlbs. Despite the sand well receiving an additional 0.7 mmlbs of proppant, the LWC well still had an incremental cost of $500,000. Fig. 8 depicts the production profile of the high conductivity LWC well (red) vs the low conductivity sand well (green), while they were produced with the same pressure management scheme.
Fig. 8. Offset Eagle Ford Shale wells illustrating the value of increasing fracture conductivity. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)
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The Bakken formation is an oil play that extends more than 200,000 square miles throughout North Dakota, Montana, Saskatchewan and Manitoba. It is a Mississippian/Devonian age formation with a majority of the activity targeting the Middle Bakken and Three Forks formations, although there are other intervals that are productive (Rankin et al., 2010). The Middle Bakken is a dolomite sequence containing interbedded siltstones and sandstones and is considered to be a tight formation, with permeabilities ranging between 0.1 and 0.01 mD. While the Bakken has been under varying degrees of development for many years, the introduction of horizontal drilling with multiple transverse fractures has led to a dramatic increase in activity in the play in recent years. In this example, a data-driven artificial neural network model was used to identify important parameters in the study wells, leading engineers to optimize on completion variables that have the biggest influence on production (Shelly et al., 2012). During the development of the data driven Bakken model, engineers selected 40 wells obtaining data commonly captured in mud log, completion, frac and production reports (Fig. 9). These wells were spread out across five North Dakota counties and were of varying lateral lengths, fracture stages, fracture fluid types, proppant types and volumes. As is typically the case in most unconventional developments, openhole logs were not run in these horizontal wells so parameters typically used to classify reservoir potential – such as porosity, water saturation, and permeability – are simply not available. However, data that is commonly available in mud reports, such as gas shows, gas fractions and mud weights, along with such parameters as formation top and formation thickness, have been identified as excellent proxies for reservoir quality. The data driven model uses a feed forward artificial neural network (ANN). The Bakken model was originally trained using 30 of the dataset wells, holding the remaining 10 wells out for model validation purposes. The ANN is trained by back-propagating variables to match two known production outcomes, in this case “Peak Oil Month” and “Oil Recovery”. Many tens of thousands of model variations are executed in developing the model that best
predicts production based on the readily available data. The data is reduced down to only the most important variables, referred to as predictors, which is typically in the neighborhood of 10–15 parameters. The single model selected as the best fit is then validated with the 10 wells that were held out of the training. Once the Bakken data driven model is selected and validated, sensitivities can be performed to determine the impact of each predictor on the two production outcomes. The predictors consist of several controllable parameters related to completion and fracture treatments, as well as non-controllable parameters that are reservoir quality driven (Fig. 10). In the case of the Bakken model, three of the top five most impactful parameters are noncontrollable – butane value, methane value and total gas volumes in the mud log. All three have strong correlations to high oil production. It is no surprise that reservoir quality is a primary driver of well performance. However, there are also many predictors that have a good correlation to strong oil production which are controllable. The number of fracture treatments, proppant (conductivity), perforation and staging lengths all have strong correlations to production. Once trained, the Bakken data-driven model adds value by being able to quickly optimize the completion design using data readily available during the drilling process, and subsequently running multiple sensitivities to the completion and fracture parameters prior to final completion design. In this example, a 9400 ft horizontal lateral was drilled, with total mud log gas averaging 2400 units and C1/C4 gas ratio of 3, indicating the wellbore was located in high quality reservoir rock. The operator's initial design (prior to running the Bakken ANN model) called for 40 fracture stages utilizing a total of 5,300,000 lb of Tier 1 20/40 ceramic proppant placed with approximately 95,000 bbls crosslinked fluid and 9500 bbls linear gel for a total cost of $6,600,000. The Bakken ANN model was then used to “optimize” the completion. Results of the model concluded that greater Net Present Value could be obtained by reducing treatment volume and the number of stages from 40 to 30. These changes were predicted to result in modest production impacts, yet when considered along with the cost savings, yielded the greatest net present value (Fig. 11). These recommendations were then applied to the actual completion design and the production achieved a best month rate of approximatly 36,000 bbls oil, resulting in one of the best wells in the area. Another application of the model is to take a well that has already been completed and placed on production, and use the model to run sensitivities to alternative completion options to determine the value of the decisions. For example, in another well, the model was initially run to ensure it matched actual production. Then, sensitivities were run first to identify the value of utilizing the LWC over white Sand, and then to identify the optimal
Fig. 9. Summary of data for Bakken data-driven model development (Shelly et al., 2012).
Fig. 10. Impact of Bakken controllable and non-controllable model parameters (Shelly et al., 2012).
After 100 days of similar choke management, the LWC well produced 15% more oil and 11% more gas, resulting in an overall 14% increase in BOE. Once again, assuming $85/bbl oil and $3.50/ mscf gas, and using actual production, after 100 days the LWC and sand wells generated gross values of $5.7 and $5.0 million, respectively. Considering the additional $500,000 investment to provide the increased conductivity the LWC well pays out the gross investment in 45 days and generates an incremental $200,000 in value over the Sand well after just 100 days. 4.4. Bakken
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completion strategy (Fig. 12). In this well, by using 100% LWC an additional 50,000 BOE were produced, creating an additional $1.5 million in NPV and improving the ROI by nearly 20%. Also, it was projected that if the stage count had been increased to 30, crosslinked fluids employed and 100% of the proppant converted to 20/40 LWC, the well could have produced an additional 200,000 BOE more, created nearly $6 million in incremental NPV and increased the ROI by 35%.
Proppant selection is driven by several parameters:
5. Summary
Conductivity is the product of proppant pack permeability and fracture width.
Reference Conductivity is measured in the lab using the ISO/API Conductivity Test Procedures.
While useful for gross comparisons, ISO/API Reference Conductivity results do not represent the realistic conductivity of the fracture at downhole conditions.
Fig. 11. Evaluation of completion parameters for optimized net present value (Shelly et al., 2012).
○ Proppant availability can drive selection choices in periods of high demand. In addition, high demand may promote an influx of inferior proppant quality within all three proppant tiers. ○ Fluid selection can drive proppant selection – when using low viscosity fluids, smaller proppant sizes will be required due to poor proppant transport and narrow pumping widths, while high viscosity fluids create wider fracs and can transport larger sized proppant, allowing for more conductivity to be placed. ○ Conductivity requirements typically drive proppant selection, and are impacted by downhole flowing conditions which must be accounted for with sophisticated fracture models. In unconventional reservoirs, these include Non-Darcy and multiphase flow, flow convergence of transverse fractures near the wellbore, low proppant concentrations, higher temperatures and embedment to name a few. Despite their low permeability, in most unconventional reservoirs hydraulic fractures are conductivity limited, meaning that an increase in conductivity will yield an increase in production. ○ Proppant selection is ultimately driven by a cost-benefit analysis, called Economic Conductivity, which should be used to select the proppant that provides the optimal combination of conductivity benefits (increased production and EUR) and cost. For many, proppant selection in today's unconventional reservoirs is often driven by upfront cost saving opportunities. However, investments in conductivity have been demonstrated to payout early in the production life of the well and provide incremental value which will improve the production, EUR and the ultimate economics of the well. Data Driven modeling in the Bakken and other formations can be applied to quickly optimize completion and fracture treatment execution by utilizing data that is easily obtainable in horizontal well drilling, and to maximize the net present value of the well.
Fig. 12. Modeling of an existing Bakken well completion/performance. Incremental value and higher ROI realized by using higher conductivity proppant in original design. In addition, significant upside potential was observed when increased stages, crosslinked fluid and higher conductivity were employed.
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