Data driven techno-economic framework for the development of shale gas resources

Journal of Natural Gas Science and Engineering 72 (2019) 103007

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Data driven techno-economic framework for the development of shale gas resources J. Chebeir a, H. Asala b, V. Manee a, I. Gupta b, J.A. Romagnoli a, * a b

Cain Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA, 70803, United States Craft and Hawkins Department of Petroleum Engineering, Louisiana State University, Baton Rouge, LA, 70803, United States

A R T I C L E I N F O

A B S T R A C T

Keywords: Tight unconventional reservoirs Supervised machine learning MINLP model Neural networks Production modeling

This work presents a data driven techno-economic framework that combines upstream, midstream and down­ stream operations to optimize profitability of shale projects. This framework is illustrated using a shale gas supply chain structure planned for development and integration in an over-supplied gas market. Field devel­ opment strategies are developed based on applying machine learning techniques to an existing field. Alternative development strategies are implemented on the integrated production-modeling platform RESOLVE–REVEAL to simulate hydrocarbon/water production. Long-short term memory (LSTM) neural networks are developed to predict gas demand and freshwater availability. Long-term strategic planning is achieved using a mixed-integer non-linear programming (MINLP) formulation. Results indicate a net present value (NPV) of 205.56 MMUS$ for optimal infrastructure design, gas and liquids transportation and distribution, and water management after 54well integration. Additionlly, results provide an optimal gas storage schedule that supports shale asset profit­ ability. Application of this techno-economic approach improves profitability projections for shale enterprises.

1. Introduction The development of shale gas resources is a multi-dimensional task due to the variety of operations related to its exploitation, refinement, sale and distribution. These operations rely on logistic decisions and technical considerations, which ultimately influence shale gas project profitability. For instance, the water management structure, number of well-pads, drilling/stimulation strategy, transportation infrastructure and costs, and surface production facilities all play a crucial role in determining the NPV of any shale gas project. Cafaro and Grossmann (2014) first performed optimization studies related to shale gas development operations. Their work focused on the intricacies of supply chain network design and operation. Following this, several authors incorporated well-pad design and operations for the economic evaluation of shale gas development projects (Wilson, 2012; Wilson and Durlofsky, 2013; Barree et al., 2014). Gao and You (2015) suggested a comprehensive framework for shale gas development. By utilizing a case study, they illustrated an optimization procedure for shale gas supply chain and frac water management networks. They employed a multi-objective non-convex MINLP model for economic optimization. Although a major milestone was attained in the

development of this decision-making tool, again, the complex non-linear processes involved in upstream operations was significantly under­ mined. Guerra et al. (2016) discussed long-term planning tools for the techno-economic evaluation of shale gas projects. In their work, they �n et al., leveraged on published reservoir simulation results (Caldero 2015) for off-line integration with a mixed-integer linear programming MILP model. The main limitation of this work resides in the over­ simplification of well-pad designs and field development schedules. In addition, the importance of implementing re-stimulation operations was not addressed. Re-stimulation of unconventional reservoirs is essential to improving shale well productivity. It is especially important in shale or tight sand reservoirs due to the sharp decline in productivity after the onset of production. In general, shale wells experience gas decline rates of 20–40% per month during the first few months and stabilize to 5% per month after 20 months of production (Asala et al., 2016). While plan­ ning shale gas projects, considerations must be given to alternative development strategies that are practical, economic, yet optimize esti­ mated ultimate recovery (EUR). Market demand fluctuations must also be considered for NPV projections. Since unconventional resource exploitation relies on hydraulic fracturing and/or re-fracturing, optimal water management structures

* Corresponding author. 3315N Patrick F. Taylor Hall, Louisiana State University, Baton Rouge, LA, 70803, United States. E-mail address: [email protected] (J.A. Romagnoli). https://doi.org/10.1016/j.jngse.2019.103007 Received 1 July 2019; Received in revised form 15 September 2019; Accepted 20 September 2019 Available online 24 September 2019 1875-5100/© 2019 Elsevier B.V. All rights reserved.

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Journal of Natural Gas Science and Engineering 72 (2019) 103007

must be carefully integrated during planning. Yang et al. (2014) pre­ sented a meticulous treatment of water management design and oper­ ations applicable to shale field development. Using a two-stage mixed-integer linear programming (MILP) model, they arrive at the optimal water management infrastructure and optimal water re­ quirements essential to their supply chain case study. They also illustrate wastewater disposal and/or treatment/reuse options while considering �n et al. (2016) also presented frac water source fluctuations. Lira-Barraga a two-stage MILP model based on flowback and water requirement un­ certainty. Yang et al. (2015) implemented a MILP formulation for water acquisition and wastewater handling. In his work, accounting for gas sales and gas costs maximized profit. Such gas costs include impound­ ment, piping, water treatment infrastructure, freshwater, water pump­ ing, wastewater treatment, and wastewater disposal. Bartholomew and Mauter (2016) incorporated an extension of this model to show flexi­ bility in water transportation and storage. Gao and You (2014) addressed water resource optimization by utilizing a mixed integer linear fractional programming (MILFP) model. With the objective of minimizing freshwater consumption per unit profit, their approach considered multiple management options including disposal, central­ ized wastewater treatment (CWT) facilities, and onsite treatment facilities. Reservoir simulators may be used to model discrete fracture

networks and key physical mechanisms that may control the overall performance of horizontal wells and shale reservoirs (Mayerhofer et al., 2006, 2010; Cipolla et al., 2008, 2009a, 2009b). In the simulator, realistic field development strategies - drilling and stimulation – may be implemented in order to derive representative field fluid production. Design parameters such as well/frac stage spacing, well lateral lengths, stimulation size, and re-frac candidates, drastically impact oil and gas recovery from a shale play. In the area of interest (AOI), the impact of these parameters, among others, may be investigated and used to construct best field practices. Such learning’s may be integrated with data driven outcomes to suggest successful field development strategies. Data driven approaches have demonstrated their effectiveness as a supporting tool for re-stimulation operations and well performance forecast (Fulford et al., 2016; Gu et al., 2016; Asala et al., 2017). Adopting a robust decision-making framework can lead to successful techno-economic evaluations of unconventional reservoir development projects. The optimization framework proposed in this study is a multidisciplinary approach that integrates reservoir simulation, datadriven techniques and mathematical programming methods. This approach is aimed at establishing long-term development strategies and logistics that maximize the economy of planned shale gas ventures. The proposed framework and its different features are detailed in the next section of the manuscript.

Fig. 1. Schematic representation of techno-economic framework. 2

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2. Techno-economic framework

CFLOWt ¼ PROFITt þ

Fig. 1 illustrates the data-driven framework adopted in this analysis. It can be divided into two main sections. Section 1 entails all activities related to upstream operations. It involves reservoir simulation to determine gas and water rate profiles according to three alternative field development strategies. Each strategy relies on input from best field practices (Drilling Info database data for the Marcellus AOI) and re-frac candidate selection outcomes described in Asala et al. (2019). Herein, an integrated machine learning approach, utilizing a feed forward neural network (FNN), a random forest classifier, support vector regression and t-Distributed Stochastic Neighbor Embedding (t-SNE) map was used for re-frac candidate selection. Section II focuses on the implementation of a strategic planning (SP) model based on a MINLP formulation. This model determines the optimal design and operation of the supply chain to ensure the alloca­ tion of natural gas and its processed by-products to different consump­ tion points. Inputs to the model include gas and water production profiles from Section 1, local natural gas demand predictions, freshwater availability predictions, water management and natural gas infrastruc­ ture, and transportation considerations. LSTM neural networks are implemented to predict freshwater availability and natural gas demand. Details of section 1 and 2 are discussed in the following subsections.

t2T

CAPEXt

ð1 þ drÞt

1

8​t2T

CAPEXt ¼ Cwellt þ Cpipet þ Cwmt þ Ccompt þ Cspt ​ ​ ​ ​ 8 ​ t 2 T

(2) (3)

where variables PROFITt , TAXt , Cwellt , Cpipet , Cwmt , Ccompt , Cspt and parameter Depτ;t are described in detail in the SID. The Gas Section involves the different phases including production, transmission and distribution of natural gas and heavier hydrocarbons (Fig. 2). The production phase includes the selection of an optimal field development strategy for the exploitation of gas resources and the gas production in the different well-pads, as described by Eqs. (4) and (5): X YSRst � 1 (4) st2ST

X RGPwp;t ¼

YSRst ⋅wprwp;st;t

8 ​ wp 2 WP; t 2 T

(5)

st2ST

where YSRst is a binary variable that equals 1 if a field development strategy st is selected for the reservoir. RGPwp;t is the raw gas production, in 106 m3 per day, at well-pad wp in each time period t. wprwp;st;t is the production rate in 106 m3 per day at well-pad wp when field develop­ ment strategy st is implemented during time period t. This parameter is determined by each strategy during reservoir simulations. The raw gas produced is then transported to compression stations, which is given by Eqn. (6). X RGPwp;t ¼ FRCwp;cn;t 8 ​ wp 2 WP; t 2 T (6)

This SP optimization model is the core of the proposed optimization framework and is composed of three major parts: a main financial sec­ tion, a gas section and a supporting operations section. The financial section of the optimization model determines the economic feasibility of the shale gas project by maximizing the NPV for the entire life cycle of the investment (Eqn. (1)). This section is also described by Eqs. (S1)–(S26) in the supplementary information docu­ ment (SID). It includes estimation of different costs including the capital and operational expenditures of different facilities and water manage­ ment infrastructures. Royalties and state/national taxes must be also determined. Finally, revenues, profits and cash flow generated during the plannig horizon of shale gas project are determined. Max ​ NPV ¼

TAXt

τ

2.1. Strategic planning model

X CFLOWt

X Depτ;t ⋅CAPEXτ

cn2CN

where FRCwp;cn;t is the raw gas, in 106 m3 per day, sent from well-pad wp to compression node cn during period t. Individual component flows can be also defined for the gas transported to each compression node (see Eqs. (S32)–(S36) in the gas section of SID). A flow balance can be also established in each junction node, as described by Eqn. (7): X X FRCwp;cn;t ¼ FRPcn;p;t 8 ​ cn 2 CN; t 2 T (7) wp2WP

p2P

where FRPcn;p;t is the raw gas in 106 m3 per day transported from compression node cn to processing plant p in each time period t. Indi­ vidual component flows can be also defined for the gas transported to each compression node according to Eqs. (S37)–(S46). See gas section of SID.

(1)

where CFLOWt is the operating cash flow and CAPEXt is the total capital expenditure. dr is the discounted rate. Those variables are determined by Eqs. (2) and (3):

Fig. 2. Phases of a typical shale gas supply chain network. 3

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Flow balances can be established in each processing plant, which is given by Eqn. (8): X X X � 1 ωp ⋅FEPcn;p;t ¼ FMPcn;p;t þ FNGIp;ip;t 8 ​ p 2 P; ​ t 2 T cn2CN

cn2CN

t-SNE (Maaten and Hinton, 2008) is a dimensionality reduction algo­ rithm used to reduce high dimensional data to low dimensions, typically 2D, while preserving both the local and global structure of the original data. Techno-economic considerations were consequently applied. A random forest classifier algorithm evaluated the order of importance of parameters relevant to well classification. Based on the integrated ma­ chine learning procedure employed in Asala et al. (2019), successful re-frac wells were used to define realistic field development strategies, whose ultimate potential is evaluated in a reservoir simulator. The analysis revealed that one-third of existing (mature) wells will success­ fully complement field development via re-fracturing. Applying this ratio to the adjacent field (current case study), we assume 18 wells out of 54 wells will be successful future re-frac candidates. The designated 18 re-frac wells are chosen based on technical similarities between re-frac wells in the adjacent brown field. Parameters like well spacing and location, as well as remaining estimated-ultimate-recovery (EUR) per well can be used to assist designation of planned re-frac wells. Such outcomes may be revised after few periods into the planning period, following techno-economic considerations discussed in Asala et al. (2019). The supervised machine learning approach applied previously also gave indications of the best field practices that worked in the AOI. Such field practices were used to develop relevant development strate­ gies employed in this work. The three development strategies utilized in this case study are presented in Table 1. Pad development periods are modeled in a reser­ voir simulator based on the associated durations for 4–6 well-pads in the Marcellus – drilling, fracturing, production and re-fracturing. Lateral landing placement for each well is based on well logs and reservoir optimization studies. A two-month period spanned the time taken for drilling, fracturing and onset of initial production in pads 2, 6 and 8 for all development strategies outlined below. This is because shorter hor­ izontal wells are drilled in these pads and this allowed for quicker turnaround time compared to the other well-pads. All well-pads are drilled in batches, based on proximity of well-pads and other logistic considerations. Each pad contained 6 wells. Drilled wells are fractured either concurrently or sequentially depending on Table 1. Concurrent fracturing utilizes zippers for an increase in operational efficiency. We utilize optimal stage design configurations of 25, 30, 35 and 40 during hydraulic fracturing. For the adjacent producing development area, stress shadow in­ vestigations using Abaqus/Standard Extended Finite Element Method (XFEM) gave insights to the range of fracture half-length, Xf, fracture width, wf, and fracture height, hf, obtainable after hydraulic fracturing. Consequently, a history matching procedure was used to obtain the final frac geometries and conductivities based on BHP’s and well production data. Micro-seismic data was not used for history matching the fracture network area due to its unavailability. For the new development area, parent fractures were modeled with an average of 3 clusters/stage (40–50 ft) in accordance with the pre­ dominant cluster spacing observed from Drilling Info database, for the Marcellus AOI. Re-fracturing was modeled by narrowing cluster spacing and utilizing an improved fracture conductivity compared to the initial fractures. Stress shadow effects were accounted for by modeling longer fractures with narrower widths. No frac hit mitigation strategy is employed in any of the development strategies as frac hits are assumed to not occur, as previously stated. Re-frac times are varied between field development strategies; I (~4 years), II (~2.5 years) and III (~3.25 years). The optimal time for re-fracturing is set based on technoeconomic considerations observed from SML results.

ip2IP

(8) 6

3

where FNGIp;ip;t represents the flow, in 10 m per day, of natural gas (constituted by methane and ethane) transported from processing plant p to interconnection point ip in time period t. FEPcn;p;t is the individual flow of ethane transported from each compressor node cn to processing plant p in period t. The remaining equations corresponding to compo­ nent flow balances in the processing plant, underground storage facil­ ities and interconnection points are presented in the SID (see Eqs. (S47)– (S55)). Equations corresponding to demand constraints and capacity design are also presented in the SID (S56–S74). The supporting operations section (Asala et al., 2019) is related to the auxiliary facilities and operations required for shale gas production. The water management is described by Eqs. (S75)–(S90) in the SID. These operations involve acquisition and transportation of freshwater during stimulation, and transportation and treatment/recycle and/or injection of wastewater during natural gas production. A basic filtration is considered in each well-pad and is not accompanied by a reduction of TDS concentration. Filtration techniques remove coarse suspended solids from the wastewater and enable unrestricted utilization in future stimulation campaigns (He et al., 2014). This implies the necessity of mixing this poor-quality water with freshwater to achieve reasonable standard conditions for fracturing operations and avoid plug-in of pores in the shale rock. The off-site treatment considers the delivery of the wastewater to centralized wastewater treatment (CWT) facilities for desalination and posteriori recycle or final disposal. Three types of technologies are considered for the water treatment including Multi-Stage Flash (MSF), Multi-Effect Distillation (MED), and Mechan­ ical Vapor Recompression (MVR). Each of these technologies experience operating limitations. In addition, the treatment costs also vary depending on the type of technology. Class II injection wells represent the last management option considered in this work. 2.2. Data driven field development strategies The field development strategies implemented in this work are based on best field practices as well as machine learning insights in the current AOI. In the AOI, the existing producing area and field planned for development are located 2 well sections (10,000 feet) apart. After field development strategies are defined, they are implemented in a reservoir simulator in order to predict gas and water production over the planning period. Gas and water production input for each field development strategy are critical for mid and downstream supply chain optimization. Below is a brief description of alternative development strategy con­ siderations and reservoir simulation implementation. 2.2.1. Infill drilling and re-fracturing considerations A field development strategy describes an appropriate schedule for well drilling, completion (fracturing, re-fracturing) and production. Robust field development strategies must address shale well-pad designs (i.e. frac stage spacing, well spacing, and re-frac time). Re-fracturing (or re-stimulation) and in-fill drilling are important components of oil and gas field development plans, and it is important to integrate develop­ ment plans for wells located in the same or similar regions. Supervised Machine Learning (SML) was applied to re-frac candidate well selection in an existing adjacent (brown) field. The result was complemented with future in-fill well drilling plans for optimizing re­ covery from the field. The SML procedure utilized a trained feed-forward neural network (FNN) algorithm and a t-distributed stochastic neighbor embedding (tSNE) map to determine well re-frac potentials in the AOI (see Fig. 3). The

2.2.2. Reservoir simulation Reservoir simulation is conducted to predict hydrocarbon and water production from each well. This is accomplished after populating petrophysical properties, fluid data, drilling schedule, and possible comple­ tion/stimulation strategies into a reservoir simulator. Reservoir simu­ lation is automated to predict production several times depending on the 4

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Fig. 3. Histogram illustrating output of a feed-forward NN showing probability of fractured wells classified as re-fractured in the Marcellus AOI (left). t-SNE visualization showing cluster behaviour of fractured and re-fractured wells in low dimensional space, based on 17 input features (right) (Modified after Asala et al., 2019).

operations - hydraulic fracturing or re-fracturing. For computationally efficient reservoir simulation, we may model wells and their completion operations individually, without considerations for interactions (i.e. frac hits, strong natural fracture interactions) across SRVs’. This is applicable when modeling flow in multi-well pads when optimal well and stage spacing determinations were made from a previous analysis. The RESOLVE platform and REVEAL simulator may be used for this scenario. RESOLVE is an integrated production-modeling tool used for solving mixed-integer, non-convex, non-linear optimization problems, posed by upstream oil and gas operations. Data objects relating to PVT and tight reservoirs are defined in RESOLVE, prior to reservoir simulation. Fig. 4 is a schematic of one field development implemented in RESOLVE® showing 54 wells distributed among 9 well-pads. REVEAL is a reservoir simulator, capable of modeling fluid flow from unconventional tight formations. Each reservoir-well model is first developed in RESOLVE, and then exported to the REVEAL simulator. In RESOLVE, PVT data objects are used to define wet gas reservoir and compositional hydrocarbon fluid properties, based on green field core data. The tight unconventional reservoir data objects allow an easy definition of well, fracture and operational parameters before exporting to REVEAL® for reservoir simulation. In REVEAL, logarithmically spacing and local grid refine­ ment may be defined to capture the transient responses close to the hydraulic fractures in the near wellbore. Fluid composition and PVT

Table 1 Summary of green field development strategies employed over 10-year planning horizon. Drilling

Stimulation

Stage/ cluster strategy Refracturing strategy

Strategy I

Strategy II

Strategy III

3750-6000-ft well laterals, batch drilled per pad, drilled with 660-ft well spacing. Batch-concurrent hydraulic fracturing of 9 well-pads.

3750-6000-ft well laterals, batch drilled per pad, drilled with 880-ft well spacing. Batch-concurrent hydraulic fracturing of 9 well-pads.

25-35 stages, 125-ft stage spacing.

30-35 stages, 125-ft stage spacing.

3750-6000-ft well laterals, batch drilled per pad, drilled with 880-ft well spacing. Sequential hydraulic fracturing of 9 wellpads. 30-40 stages, 125-ft stage spacing.

Based on re-frac selection criteria, 18 parent wells are re-fractured over the 10-year horizon. No re-frac hit mitigation strategy is employed.

number of wells and strategies evaluated. The results of reservoir simulation are exported as input for the SP optimization model. In tight shale formations, drainage from the reservoir is from a stimulated reservoir volume (SRV). For shale wells, this SRV is limited and defines the drainage extent of each infill well after stimulation

Fig. 4. Schematic showing assembly of 54 tight reservoir data objects and 7 PVT data objects on the RESOLVE platform. 5

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data may be re-verified. Due to the spatial variation of methane composition in the field, single component Langmuir isotherm param­ eters are used to model adsorbed shale gas composed of CO2, H2S, n-C1 to n-C5, i-C4, i-C5, C7þ. According to Fig. 4, well-pads 1 and 2 possessed similar gas composition as well as well-pads 7 and 9. An elliptical fracture geometry is used to model fractures for the new wells. Assuming the same stimulation program was employed in the producing and planned development areas, we employ a similar range of history-matched fracture half-length, Xf, fracture width, wf, and fracture height, hf, for simulating production in REVEAL. Relevant fracture and reservoir parameters are defined in Table A1. REVEAL reservoir simulator was chosen for this study because of its capability for compositional modeling, reduced computational cost and its ability to use existing reservoir simulation models as starting points for integration studies. Fig. 5 is a zoomed-out view of the superstructure (congregation of well-pads 1–9) in a CMG reservoir model. The X–Y view is a 2-D aerial view of the reservoir model illustrating the structural heterogeneities and the relative placement of wells in the model.

2.3.1. Natural gas demand Natural gas demand at each of urban conglomerate is determined by disaggregating total demand into residential, commercial and industrial demands. These demands are then predicted separately by LSTM algo­ rithms, which utilize five fundamental predictors as input. For each demand category, the five predictors used are: historical natural gas demand, natural gas price, WTI crude oil price, regional temperature, and population of natural gas consumers. Historical natural gas demand was chosen as a predictor due to the strong periodic trend observed with past and current natural gas de­ mand. The relevance of the natural gas price as a predictor is a conse­ quence of the practical law of supply and demand – gas demand falls when the price goes up and gas demand increases when its price picks back up. Thus, price has a direct impact on observed natural gas demand fluctuations. Crude oil price was considered an essential predictor because it serves as a fundamental energy substitute for natural gas utilized by industrial and commercial users. Local temperature has a considerable effect on the seasonal variation of natural gas demand. This is because in the winter, increased usage of heating devices shoots up demand while the reverse is observed for summer weather conditions. The amount of natural gas consumers in the region directly impacts demand growth, therefore regional population is an important predictor for natural gas demand forecast. Historical data for each predictor var­ iable was available for different lengths of time, however, for timespan uniformity, available data for each predictor was truncated from January 1991 until September 2017. Data utilized for natural gas de­ mand prediction was obtained from EIA (2018). In the case of residential and commercial demands, the neural network models were constructed by stacking two LSTM layers, fol­ lowed by a layer (FNN) with sigmoidal activation and finally a layer (FNN) with linear activation. The layers were comprised of cells/nodes with a 40-40-60-1 configuration. The artificial neural network was trained with 90% of the available data and then tested on the remaining 10%. During testing, the network achieved an accuracy of 96% with a deviation of � 2%. The model was run until the validation accuracy flattened out which occurred at the 61st epoch. The neural network model for industrial demand comprised of three LSTM layers, followed by a linear layer. The neural network training was accomplished in a similar manner as described above. The layers were comprised of cells/ nodes in a 40-60-60-1 configuration. Fig. 6 compares predicted

2.3. Forecasting models The natural gas demand and freshwater availability represent critical input data of the SP model. The prediction of these parameters during the planning horizon is based on the utilization of recurrent neural networks (RNN). These types of neural networks have a dynamic approach to processing inputs. The elements of the input array are fed to the network one after the other in chronological order. The output vectors are generated dynamically in response to both the corresponding input and the state of the network, in contrast to a more static approach as in the case of FNN. A major limitation of the RNN is the inability to look too far back into the past, which is typically attributed to either the vanishing gradient or the exploding gradient problem. When the weights of the RNN take on large values during the training phase, the error gradient tends to grow exponentially resulting in the exploding gradient problem. Hochreiter and Schmidhuber (1997) solved this by replacing conventional neurons with specially designed gated cells that enforce constant error flow. This special type of RNN is called LSTM neural networks and is widely implemented in modeling sequential data. Thus, this neural network structure is selected to predict the gas demand and water availability in the AOI.

Fig. 5. X–Y view of CMG model superstructure illustrating the relative locations of well-pads 1–9 in the “green” field under development (bar scale on the right in ft). 6

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Journal of Natural Gas Science and Engineering 72 (2019) 103007

(training) and actual gas demands and goes on to show forecasted de­ mand based on the trained neural network. In addition, the same figure summarizes the grid search experiments performed during the neural network optimization. After total demand prediction, a predetermined regional factor based on the number of customers is utilized to deter­ mine the demand in each consumption point. The three models described above were optimized using the following procedure. Each model was initially set up with a single LSTM layer. The number of nodes/cells for this layer was selected from a grid of size 10 to 100 in steps of 5 and the number that produced the lowest validation loss was selected as the optimal number of nodes/cells. This method was repeated for each new layer added to the model. When the addition of new layers failed to show an appreciable decrease in vali­ dation loss, the optimization was stopped, and the resulting configura­ tion was used in the final model. The weights of the network were initialized using Xavier’s initialization scheme (Glorot and Bengio, 2010) to ensure that the back-propagated error neither vanished nor blew up during the training process. The LSTM cells were used in a stateful mode to preserve the cell-memory between batches. As can be observed in Fig. 6, the model predicts the seasonal pattern and periodicity of the series with accuracy. However, it has difficulties in predicting some of the peak points accurately. This may be a conse­ quence of the large variance of the peak data points. During training, the model first identifies the dataset range (normalized between 0 and 1), before identifying the seasonality and periodicity of the entire dataset. Then, it begins to model more complex features of the data like the peak points. Since the total number of peak points represent only a minor fraction of the total number of points, the model is not able to minimize the error function of the peak points with as much accuracy as the nonpeak data points.

case the of natural gas demand, the peak points are not accurately determined by the model due to the procedure utilized to perform the prediction. Additionally, the same figure summarizes the grid search results obtained for the LSTM configuration optimization. Spatial aver­ ages of run-off water are determined for the freshwater sources. The product of predicted run-off and the catchment areas of each water source results in total water availability. 3. Case study This case study considers exploitation of shale resources in the liquid-rich region of the Marcellus play. 54 additional wells are planned for development and integration to a nearby existing supply chain network that was initially based on 18 existing wells and 36 planned wells. In this work, we consider a distribution system that extends across several counties in the state of Pennsylvania. Fig. 8 illustrates the shale gas superstructure developed for this case study. This superstructure was developed based on information obtained from gas operators and gas distribution companies in the region. The shale gas section is described by: well-pads (wp 2 ​ WP), po­ tential compressor stations for moving gas from well-pads to processing facilities ðcn 2 CNÞ, processing plant locations for raw gas treatment and separation ðp 2 PÞ, interconnection points in the existing distribu­ tion system ðip 2 IPÞ, underground storage facilities located in the re­ gion ðuf 2 UFÞ, consumption or delivery points in the region ðm 2 MÞ, and potential cracker locations for liquid ethane consumption ðe 2 EÞ. Raw natural gas and liquid ethane are transported by a pipeline system, throughout the supply chain network. Heavier liquid hydrocarbons including propane, butanes and natural gasoline (C5, C6, Cþ 7 ) are commercialized at the gate of the processing plant. Flow direction in the existing distribution system goes from south to north of Pennsylvania. Underground storage facilities provide sufficient flexibility during unfavourable periods of demand and supply. Two consumption points are located in urban areas while a third one is located at a power plant in the region. Standard costs are charged for transportation and storage infrastructures utilized during the planning horizon. Water is transported by tank-trucks using existing road networks. The water management structure is described by potential surface freshwater sources ðfw ​ 2 FWÞ, freshwater impoundments ðim ​ 2 ​ IMÞ, injection wells ðiw ​ 2 IWÞ, and existing CWT facilities ðc ​ 2 ​ CÞ, located within the superstructure. The reuse limits of wastewater (TDS content) guide the water management options adop­ ted. RNN algorithms forecast freshwater availability during the planning horizon. ArcGIS mapping platform is used to describe watershed loca­ tions and the distance between nodes in the superstructure (ESRI, 2014). We consider continuous withdrawal from freshwater sources with a minimal impact on stream flows. Consequently, minimal withdrawals (due to low flows) are considered during the summer and fall and

2.3.2. Freshwater availability To determine the amount of freshwater available in each source of the AOI, their average run-off must be predicted. Two LSTM layers bounded by two layers with rectified linear unit (ReLU) activation make up the prediction model. The optimal model configuration is determined using the procedure described in Section 2.3.1 yielding a 40-30-28-1 neural network configuration. The predictor variables used as input to the model include snowpack, evapotranspiration, precipitation, runoff, soil moisture, and change in storage. The data points from the different freshwater sources are split into batches of size 30. The batches are normalized, and their Z-scores are utilized as inputs to the LSTM model. Normalization is performed to improve the efficiency of the gradient descent algorithm. The model is then repeatedly run until the loss function flattened out, showing no signs of dropping further, and this occurred at 400 epochs. The R2 value (accuracy) for the predictions is 0.952 (�0.01 deviation). Fig. 7 compares actual freshwater run-off with predicted run-off at Source I as well as its forecasted run-off. Similar to

Fig. 6. Comparison of actual and predicted commercial natural gas demand in the first 10 years followed by a 10-year demand forecast (left). Grid search experiment to optimize number of LSTM layers (right). 7

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Journal of Natural Gas Science and Engineering 72 (2019) 103007

Fig. 7. Comparison of actual and predicted run-off in the first 10 years followed by a 10-year demand forecast (left). Grid Search experiment to optimize the number of LSTM layers (right).

Fig. 8. Shale gas superstructure showing potential and existing infrastructure (ESRI, 2014).

maximum withdrawals (due to high flows) are contemplated during the spring and winter sessions (Abdalla and Drohan, 2018). Wastewater generated during the stimulation/re-stimulation and well production is called flowback and produced water, respectively. In this work, flow­ back wastewater is reintegrated into water management based on 25% of water injected during well stimulation/re-stimulation (Vidic, 2015). Produced water is obtained as associated water during reservoir simu­ lation operations. The selection of the planning horizon length plays a huge role in the successful execution of a venture (Sołoducho-Pelc, 2015). The long-term

strategies for the case study presented utilize a typical planning period of 10 years. In order to account for the rapid decline in well gas production rates and in order to capture important field development periods, the planning horizon is discretized into 120 months (time periods). 4. Results and discussion In this section, the results obtained by the implementation of the proposed data-driven optimization framework are summarized. The MINLP formulation developed in this work is implemented on the GAMS 8

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Journal of Natural Gas Science and Engineering 72 (2019) 103007

platform utilizing the global optimization solver, BARON (Tawarmalani and Sahinidis, 2005). MINOS, SNOPT and CONOPT are the non-linear sub-solvers used in this package while CPLEX is used as the default linear solver. Model convergence is achieved in 24416.26 CPU seconds after 22 iterations and with a relative gap of 0.03. The relative gap is an indication of the optimal integer solution with respect to the objective function.

of production is distributed between storage facilities uf2 and uf3 and the consumption points m1 and m2. 4.2. Underground gas storage and water management The variation of gas demand, underground gas storage and price over the planning horizon is depicted in Fig. 10. In order to balance gas supply and demand at consumption points I, II and III, and at favorable gas prices, intermittent underground gas storage is necessary. At the beginning of the planning horizon, an oversupply is observed due to the rapid increase in gas production after new well stimulation. The with­ drawal and delivery operations are performed during periods that gas

4.1. Optimal network design and operation Field development strategy III is selected as the optimal strategy for exploitation of shale gas resources from the liquid rich region of interest. This strategy maximizes cumulative production from the reservoir and adjusts best to exogenous parameter variations such as regional water availability, natural gas demand at different consumption points and energy commodities’ prices. The locations selected for the different compression stations mini­ mize the capital expenditure required for the construction of the gath­ ering pipelines to transport shale gas from the well-pads to the different points of compression. In a similar fashion, the location of the processing plant minimizes the capital investment required for the gathering and transmission pipeline infrastructure. The processing plant p1 is strate­ gically located in the middle of the shale gas development (surrounded by the production areas) and relatively close to an interconnection point (ip2) in the distribution pipeline infrastructure (Fig. 9). This ensures the minimization of the distances with the consequent reduction of capital required for the transportation. In comparison with p1, the other two potential locations for the processing plants are too far from some or even all the production regions and involve an enormous capital in­ vestment for the gas transportation. From interconnection point ip2, part of the natural gas production is transported to the closest con­ sumption point (m3) during most of the planning horizon. Other portion

Fig. 10. Gas demand and storage variations during the project plan­ ning horizon.

Fig. 9. Optimal supply chain network design (ESRI, 2014). 9

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Journal of Natural Gas Science and Engineering 72 (2019) 103007

demand and prices peak. Once the productivity rate in different pads declines severely, gas demand starts to be less significant in the storage decisions while gas price starts to have a critical impact on the projects profitability. The increase in the amount of gas stored from period 63 to 92 is related to the increase in production generated by re-frac opera­ tions performed in well-pads in wp5, wp7 and wp9. Part of this excess production is stored and delivered in later periods when the price con­ ditions are more favorable. Selection of freshwater sources is based on the closeness of these sources to different well-pads and source water availability during pe­ riods of well stimulation. Freshwater is trucked from source fw3 to wellpads wp1, wp2, wp3, and wp4, from source fw1 to well-pads wp4, wp5, wp6, wp7 and wp9, and from source fw2 to well-pads wp4 and wp8. The water transportation to well-pads wp1, wp2, wp3, wp5, wp7 and wp9 is explained by the necessity of minimizing the distance and the conse­ quent transportation costs. In the case of the well-pad wp4, the water cannot be fully allocated from fw2 due to the lack of water availability in this source when stimulation operations take place in this pad (dry season). Then, the two other sources have to provide the required water during stimulation of new wells. A similar situation is observed in the well-pad wp6, freshwater source fw1 cannot fully satisfy the water re­ quirements of this pad. To solve this issue, an impoundment (im1) has to be constructed to store the water utilized in well-pad wp6, which is initially transported from the closest freshwater source (fw3). This impoundment is built and operated for water storage coming from different freshwater sources as well as recycled water received from offsite treatment sources. In addition, a minimum amount of water is transported from this impoundment to the well-pads wp2, wp3, wp7, wp8 and wp9. Flowback generated after well stimulation receives on-site treatment and is mixed with freshwater for use in future or nearby stimulation activities. Onsite water treatment in different well-pads reduces the cost that would have been incurred by transporting large quantities of wastewater to CWT facilities. However, a small amount of produced water is transported off-site for treatment/recycle and/or discharge. Given the high cost of MVR technology in comparison to MED, the CWT facility c1 utilizes the MED technology for water desalinization. Wellpads wp1, wp2, wp3, wp4 and wp5 utilize the CWT facility c1 for the treatment of produced water during well production. When TDS con­ centration limits exceed stimulation water specifications, Class II in­ jection wells are utilized. Results indicate that wp1, wp2, wp3, wp4 and wp8 utilize iw1 while wp5, wp6, wp7, and wp9 utilize iw2.

Fig. 11. Cost breakdown for shale gas supply chain network.

any type of variation in state legislations such as increase of tax rates and/or incorporation of new regulations can have a direct impact on the economy of a shale gas venture and jeopardize its possibilities of success. Because taxes are charged over the profit generated [8,9], most of this cost is concentrated in the first half of the planning horizon when the highest levels of gas production are achieved. The case of the royalties is different because it is charged over the revenues generated by the gas operator during the entire life cycle of the project. The operating costs (OPEX) account for only 5.65% of total discounted costs. A large pro­ portion of this cost corresponds to well production, processing plant, and compression station operations. Observing the variation of free cash flow (Fig. 12), a remarkable impact of the capital investment can be noticed during the first time periods of the planning horizon (from time period 1 to 12). Most of the time, the free cash flow is negative or neutral in these initial months of the project. Of course, there is not production and hence no revenue generated by the gas operator. These expenditures are related to the construction of the required infrastructure for transportation (gathering and transmission pipelines), compression and processing of shale gas and subsequent products. In addition, the drilling and stimulation campaigns of wells located in pads wp1, wp2, wp3, wp4 and wp5 are taking place during these first time periods of the planning horizon (from time period 1 to 6). Then, the free cash flow presents a slight in­ crease pattern after the first stimulation campaigns followed by a pro­ found depression in time period 12 caused by the investment related to construction of well-pads wp6, wp7 and wp9. In period 19, there is another depression in the free cash flow related to the construction of the last pipeline for transportation of gas from well-pad wp9 to

4.3. Supply chain financial projections An NPV of 205.56 MMUS$ is obtained for the development of 54 shale wells from 9 well-pads over the 10-year planning horizon. Fig. 11 depicts cost breakdown for the project. The total capital expenditures (CAPEX) due to shale gas production, transportation, gas processing and infrastructure represents 66.35% of the total discounted costs. Well drilling and stimulation accounted for 22.33% of the total discounted costs, with re-fracturing costs accounting for 38.52% of this category. The cost associated with processing plant construction is 322.32 MMUS$ (28.45% of total discounted costs) compared to pipeline infra­ structure costs of 156.87 MMUS$ (13.85% of the total discounted costs). The latter cost may have been higher if an existing transmission network was unavailable in the region. In comparison with other infrastructure, compression stations’ costs represent a much lower capital investment accounting for 16.16 MMUS$ (1.64% of total discounted costs). Simi­ larly, the construction of water impoundments involves an even lower investment, accounting for only 0.89 MMUS$ (0.08% of total discounted costs). Throughout the planning horizon, taxes and royalties constitute a significant part of the remaining expenses i.e. ~27.99% of the total discounted costs. Since each state has its own regulations with specific implications on the shale gas reservoir development. This means that

Fig. 12. Cash Flow for base case and cumulative cash flow variation for case studies I, II and III. 10

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Journal of Natural Gas Science and Engineering 72 (2019) 103007

project. Moreover, this reduction in revenues has serious repercussions on the NPV generated by the development, which is now 154.76 MMUS$ (~25.45% reduction). The payback period suffers minimal impact with an extension from 47 months (Base Case) to 50 months (Price Reduction I) of field operation. The next test involves a reduction of approximately 15.3% in the price of ethane (10% above the natural gas price) and a 20% in the prices of heavier hydrocarbons (Price Reduction II). The reduction of NPV is even higher with a value of 100.01 MMUS$ (~51.83% reduction). The payback period is extended even more from 47 (Base Case) to 54 (Price Reduction II) months. In general, when the prices of NGL are reduced, the cumulative cash flow curve is progres­ sively shifted down while the break-even point is displaced to the right. The variation of cumulative cash flow for the different case studies is observed in Fig. 12.

Table 2 Revenue distribution generated by each case study. NGL Components

Base Case Study

Case Study - Price Reduction I

Case Study - Price Reduction II

Natural Gas Ethane Propane Butanes Natural Gasoline Total Revenues

493.19 127.23 314.06 222.78 131.15

492.92 117.44 282.65 200.50 118.03

489.74 107.66 251.24 178.22 104.92

1288.40

1211.54

1131.78

compression node cn3 and the drilling of the last wells in the field. The costs related to the construction of the last pipeline infrastructure and shale wells do not have so much negative impact on the cash flow given that the number of wells already producing is high enough to generate revenue. Most of the free cash fluctuations occurring during the rest of the planning horizon are related to the injection and delivery of natural gas from different underground facilities. This pattern changes when well productivity starts to decline. Given that the costs related to well restimulation are not as significant as others, there is no visible impact of these expenditures on cash flow. Payback of the shale gas venture occurs after 47 months. This period covers the time to reach the break-even point of the project (gas operator has no net profit or loss). The total discounted revenue generated by each shale gas component is presented in Table 2. For the base case scenario, the total discounted revenue generated over 10 years is 1288.4 MMUS$. The NGL com­ modity represents 61.7% of the total discounted revenues, highlighting the impact of NGL commercialization compared to natural gas. Among the NGL’s, propane represents the second most valuable commodity on the basis of revenue generated after 10 years of production. Although propane is third highest in volumetric composition, its price (higher than liquid ethane price) compensates for its lower production rates in the field and generates 24.4% of the total discounted revenues. Butanes follow this component with 17.3% of the total discounted revenues generated at the end of the planning horizon. This is explained by its lower volumetric composition and similar price when compared with propane. Ethane revenues are low given the depressed prices accrued by this commodity’s oversupply in the last few years. The revenues generated by natural gasoline represent 10.2% of the total discounted revenue generated. Liquid ethane provides the lowest revenues with 9.9% of the total discounted revenues at the end of the planning horizon. For the shale gas venture, economic analysis of NGL’s shows a decline of approximately 7.7% in the price of ethane (20% above the natural gas price) and 10% in the prices of other heavier hydrocarbons (Price Reduction I). From Table 2, there is a reduction in the discounted revenues generated by each liquid present in the shale gas, implying a significant decrease in the total discounted revenues generated by the

5. Conclusions This work presents a data-driven, techno-economic framework for shale asset integration and planning in an oversupplied-gas energy market. A MINLP model was developed for shale gas supply chain optimization after incorporating reservoir simulations and machine learning algorithms - FNN, a t-SNE map and LSTM architectures. In the Marcellus liquid-rich region, an FNN-t-SNE based approach may be used to guide re-frac well candidature, prior to field develop­ ment strategy implementation. For any planning horizon, regional gas demand can be predicted by disaggregating into residential/commercial and industrial demand. A 40-40-60-1 LSTM RNN sufficiently predicts residential and commercial demand and a 40-60-60-1 LSTM RNN pre­ dicts industrial demand, with (0.96�0.02) accuracy in both cases. Regional freshwater availability for shale well stimulation may be his­ tory matched and predicted with 95.2% �1% accuracy, using a 40-3028-1 RNN configuration. For the base case 54-well integration study, a project NPV of USD 205.56 million was realized over a 10-year planning horizon. Analysis of project economy shows that NGL’s contribute to a greater proportion of total discounted revenues (~61.7%). Major expenditures associated with onsite infrastructure, gas facility processing and product trans­ portation give ~66.35% of total discounted costs. The results show that minimizing costs and maximizing gas resource allocation leads to optimal shale gas network designs and operations. Furthermore, exogenous-driven gas oversupply conditions and gas demand fluctua­ tions can be managed by an efficient underground gas storage schedule, programmable to favorable price conditions. Shale play profitability relies on the full integration of upstream, midstream and downstream opportunities. This work presents a cohe­ sive machine learning approach to optimize NPV from unconventional shale gas developments.

Appendix A

Table A1 Model parameters for new field development. Parameters

Value

Lateral Length, ft Formation Top TVD, ft Well radius, ft Grid Block size, ft Initial water saturation, % Reservoir Temperature, F Rock density, Ib/ft3 Compressibility, 1/psi

3750–6000 12,590 0.30 450 22–25 245 118 1.12E-05 (continued on next page)

11

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Journal of Natural Gas Science and Engineering 72 (2019) 103007

Table A1 (continued ) Parameters

Value

EOS Fracture half-length, ft Min. Bottom Hole pressure, psi Initial Pressure, psi Fracture Height, ft (max) Fracture width, ft Re-fracture width, ft Matrix porosity, % Matrix permeability, md Matrix permeability, ratio Langmuir Adsorption constant, 1/psi (CH4) Maximal adsorbed mass, g-mole/Ib (CH4) Gas components Gas Water Contact Depth, ft

Peng Robinson 200–250 450–550 6860–8443 200–250 0.01–0.02 0.011–0.025 6.2–8.9 0.0085–0.013 0.1 0.00125 0.0873 H2S, CO2, n-C1 – Cþ 7 12900–13129

Appendix B. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jngse.2019.103007.

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