Log identification method in complex sand reservoirs

M INING SCIENCE AND TECHNOLOGY Mining Science and Technology 19 (2009) 0531–0536

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Log identification method in complex sand reservoirs ZHAO Xiao-liang1, LIAO Xin-wei1, CHEN Xi2, XU Sheng-feng3, XU An-zhu4 1

MOE Key Laboratory for Petroleum Engineering, China University of Petroleum, Beijing 102249, China 2 China Petroleum Logging Co., Ltd., Xi’an, Shaanxi 710021, China 3 Sinopec International Petroleum Exploration & Production Corporation, Beijing 100083, China 4 Research Institute of Petroleum Exploration & Development, Beijing 100083, China

Abstract: In order to obtain effective parameters for complex sand reservoirs, a log evaluation method for relevant reservoir parameters is established based on an analysis in the gas-bearing sandstone with high porosity and low permeability, low porosity and permeability and on various characteristics of log responses to reservoir lithologies and physical properties in the Neopleozoic sand reservoir of the Ordos basin. This log evaluation method covers the Cook method that is used to evaluate the porosity and oiliness in high porosity and low permeability reservoirs and another method in which the mineral content, derived from geochemical logs, is used to identify formation lithologies. Some areas have high calcium and low silt content, not uniformly distributed, the results of which show up in the complex formation lithologies and conventional log responses with great deviation. The reliability of the method is verified by comparison with conventional log data and core analyses. The calculation results coincide with the core analytical data and gas tests, which indicate that this log evaluation method is available, provides novel ideas for study of similar complex reservoir lithologies and has some reference value. Keywords: complex formation lithology; reservoir parameter evaluation; Cook method; geochemical element; element content; mineral constituent; lithological identification

1

Introduction

For the most part natural gas reservoirs, widely distributed in China, are of the pore type. There are sandstone pore types in the Neopleozoic reservoirs in the Ordos basin, in the reservoirs in the Kuche depression in the Tarim basin, the Sanhu region in the Chaidamu basin and in the Yingyiqiong basin. In contrast, the cavities in the Carboniferous system in east Sichuan are well-developed due to the effects of erosion and leaching and the pores are well-developed due to dolomitisation and erosion of oolitic beach limestones. The gas-bearing reservoirs largely consist of two major categories: the pore sandstone reservoirs and the paleo-weathering crust or granular carbonate reservoirs. Hence, in our study, we have investigated the complex sandstones of high porosity/low permeability and low porosity/permeability lithologies with complicated physical properties. First of all, four relationships of these properties have been analyzed for their reservoir characteristics and reservoir parameters. The relationships are derived on the basis of core data and the different effects of the spatial patterns of the reservoirs on log responses. We have mainly used a non-electricity Cook method and

the log characteristic parameter weight method which uses conventional logs and various log response characteristics and sensibilities with logging approaches in gas- bearing sandstones to evaluate a reservoir matrix lithology, their electrical properties, porosity and other characteristics. We have used geochemical element logging data to identify the Neopleozoic calcareous sandstone in the Ordos basin[1–2].

2 Reservoir characteristics The lithology is primarily siltstone and argillaceous siltstone in Neogene formations in southwestern China. The reservoir has high shale content, a complicated shaly distribution, a mixture of grit and gravel, small pore throats and poor connectivity and is characterized by high porosity, middle-low permeability and bad heterogeneity. Owing to the effect of the sedimentation factor, we cannot identify lithologies very well with the small GR (natural gamma ray) amplitude. Electrical properties are very much affected by the lithologies and there is a small contrast in the electrical response characteristics in the gas and water layers. Since there are intermediate grits and

Received 15 November 2008; accepted 20 January 2009 Project supported by the Program for New Century Excellent Talents in Universities Corresponding author. Tel: +86-10-62093324; E-mail address: [email protected]

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gravels, low maturity of rock textures and constituents in the reservoir, as well as various grain sizes, poor sorting and high matrix content (mainly clay), the reservoir presents us with conditions of high porosity/middle-low permeability and bad heterogeneity. For the Neopleozoic, gas-bearing sand reservoir of low porosity and permeability in the Ordos basin, this group of formations consists of a sand body of river channel sub-facies, interbedded with argillaceous rock and coal seams of flooded swamp sub-facies and a tight sand reservoir of low porosity and permeability with great heterogeneity. The log response to natural gas is weak and the interpretation parameters for the gas zone are readily affected. The lithology is characterized by low porosity and permeability, strong diagenesis, many secondary pores, a considerable micro-pore and fracture effect, developed diagenesis clay minerals and scaly or tiny throats. The reservoir diagenesis is affected by mechanical compaction, cementation, dissolution, metasomatosis, etc. The capacity of the porosity and permeability is determined by the sedimentary environment of the reservoir, characteristics that accompany low porosity and permeability. There are complex lithologies, great variations of physical properties and severe heterogeneity in the reservoir in the Gasi oilfield of the Chaidamu basin. Its sandstone largely contains clay, quartz, feldspar and other heavy mineral clasts.

3

Log identification method of reservoir characteristics

For the evaluation of reservoir parameters, we primarily conducted quantitative and qualitative evaluations of the reservoir. In our conduct of quantitative evaluation, we used a Cook method, as well as an uncorrelated log optimization method, given the various physical properties of the reservoir. The qualitative evaluation of reservoir characteristics was made using the parameter weight method of log characteristic values. 3.1 Determination of reservoir parameters 1) Determination of rock constituents, matrix parameters and shale content For this high porosity and low permeability reservoir, with its shaly matrix, containing quartz, coal, gas and water, the parameters were determined based on the geology of the area, combined with an analysis of thin rock sections. The determination of rock matrix parameters in a reservoir evaluation is very important, especially for density, neutron and acoustic transit time values of the matrix, which are directly related to the calculation of its formation constituents, porosity and permeability. The matrix parameters can be derived using a cross plot of gas-corrected density, acoustics and neutron log data with the porosity from

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its core analysis. The shale content of the reservoir could be determined by GR, SP (spontaneous potential), resistivity and porosity. If a GR curve would have been used to calculate the shale content, the GR amplitude variation might have been too small to discriminate lithologies due to thin beds and high shale content as the result from sedimentation in the area. On the other hand, there are apparent lithological response characteristics on the resistivity curve. Therefore, we have used the deep lateral resistivity curve to calculate the shale content in the area[3–4]. Its calculation equations are as follows: R − Rtmin sh = t (1) Rtmax − Rtmin Vsh =

2GC1∗sh − 1 2 GC1 − 1

(2)

where sh is relative value of shale content, %; Rt measured deep lateral resistivity, ȍ ⋅ m ; Rtmin minimum resistivity of pure claystone, ȍ ⋅ m ; Rtmax maximum resistivity of pure sandstone, ȍ ⋅ m ; Vsh is shale content, %; and GC1 empirical index in calculating the shale content, it is equal to 3.7. For the low porosity and permeability gas-bearing reservoir, a non-correlated optimization method is used, i.e., different log response equations are simultaneously solved to calculate various mineral and fluid volumes. This optimization technique is used to keep the lack of correlation of the equation low, through adjusting such input parameters as mineral logging response, weight values of input curves, etc. Multiple models are simultaneously solved and a final model is obtained according to certain compound probabilities, i.e., that of the matrix mineral of the formation, the fluid volume and the calculated reservoir parameters. According to the physical attributes of natural gas, its neutron log density is small and its acoustic transit time is large. A correction for natural gas density, neutron and acoustic log values must be made before drawing the cross plots. For the gasbearing and quartz sandstone, relevant density, neutron and acoustic equations can be written in light of the contributions of the matrix, water and gas to log responses. On the cross plot of density, acoustic, neutron and core analysis porosity, the intersection points of the trend line obtained by regression with X-axis are the values of the quartz density, the acoustic transit time and neutron porosity. The shaly effect can be minimized by selecting points with low shale content and of pure lithology. Since the shaly effect would have made the equations complicated and contribute little to calculation accuracy, it is not taken into account in our calculations. We have used the cross plot method largely for the determination of shaly matrix values. M and N values are calculated according to density, neutron and acoustic log values. On the M-N cross plot, the shaly M value is determined as 0.7 ac-

Log identification method in complex sand reservoirs

response equation covers the contributions of the matrix, the immovable fluid in the pores and the mud filtrate to log responses. The neutron response equation covers the contributions of the mud filtrate, the bound water and clay to log responses and simultaneously the gas excavation effect. The porosity, calculated using the neutron-density cross plot method, is largely used to eliminate the effect of a skipped cycle caused by gas with the compensated acoustic log, leading to higher porosity. On the other hand, the Cook method uses non-electricity to calculate saturation, which eliminates a large amount of the lithological effect on the electrical properties of the high porosity and low permeability reservoir, without distinct response characteristics of fluid to the electrical properties[5–6]. For the low porosity and permeability gas-bearing sand reservoir, an empirical equation from the Schlumberger Company is used to calculate water saturation of the formation, which is involved in such key data as water resistivity of the formation, rockelectricity parameters, etc. The value of water resistivity of the formation is taken depending on the buried depth and horizon of the formation. The rockelectricity parameters are determined comprehensively on the basis of experimental data, from tentative logging calculations and comparative studies of the data, including mercury injections, relative permeability experiments, sealed coring and water saturation by conventional coring analysis. The rockelectricity parameters of the low porosity and permeability gas-bearing sand reservoir are as follows: a=1.0, m=1.95, b=1.0, n=2.27. 'HSWKP

cording to the trend in lithological variation and on the cross plot of density, neutron and acoustic values, the matrix values in the gas-bearing reservoir are 2.72 g/cm3, 0.37 (v/v) and 236.2 ȝs/ft. The parameters of the matrix are verified by combining the constituent contents of the formation and core analysis porosity. The actual calculation results indicate that the formation constituents calculated using the above parameters are in good agreement with the actual core analysis, which proves the appropriate rock-electricity parameters that have been determined. 2) Determination of porosity and saturation For the high porosity and low permeability gasbearing formation, the intersection volume model covers matrices and pores. Matrices contain the matrix and clay. Pores contain gas and water. Since natural gas has a great effect on porosity curves and its level of effect is different for each part of the porosity curves, the reservoir porosity calculated by only one porosity curve cannot exactly reflect its true porosity. In the meantime, since the electrical properties of the area reservoir are greatly affected by the lithology, there is no apparent difference in the electrical properties of gas and water zones. The reservoir saturation is calculated with the Archie equation mainly using its resistivity difference, which cannot be used to calculate exactly the saturation in the gas and water zones due to their reduced saturation difference. The density and neutron log response equations can be obtained separately using the corrected density and neutron log values and are based on the Cook intersection volume model (Fig. 1). The density (Ω ⋅ m) (Ω ⋅ m) (Ω ⋅ m)

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(ȝs/m)

  



   

Fig. 1

Comparison of Cook saturation and Archie saturation

3) Determination of permeability For the high porosity and low permeability gasbearing reservoir, the high porosity and low permeability are caused mainly by such factors as the high shale content of the reservoir, its complex shaly distributions, small grain sizes and pore throats. Professor Zeng Wenchong suggests that the permeability of the reservoir is largely related to its porosity, shale content, grain size and pore throat dimensions. The log interpretation model of permeability can be obtained by fitting a regression equation of core analysis data from various reservoir types (high porosity and low permeability, intermediate porosity and low permeability and low porosity and permeability reservoirs) in the different areas in the Shengli oilfield. Its equation is shown as follows: [D + 1.7log(MD) + 7.1logϕ ] k = 10 1

(3)

where MD is median grain diameter which is correlated with the median grain diameter and shale content, D1 is empirical coefficient which relates to the grain size and measured pore throat data from the area, ϕ is porosity[7]. In the low porosity and permeability reservoir with multiple pore types, there are intergranular pores, corrosion pores, intercrystalline pores, micro-pores and secondary microfractures that make for complicated porosity-permeability relationships. According to the actual conditions, the permeability is calculated using the porosity and permeability of the core analysis, incorporating GR and deriving multiple linear regression equations. These equations can be expressed as linear combinations of permeability using common logarithm and relative values of porosity and GR. 3.2 Identification of complex lithology 3.2.1 Correlation between formation element content and mineral content Herron et al. from Schlumberger, who carried out neutron activation and X-ray diffraction analyses on many cores, proposed a correlation between element content and mineral content in the formation. They measured the weight percentage of 21 elements through neutron activation analysis and the contents of four or six minerals in the cores through X-ray diffraction. Then they carried out statistical analyses on the contents of the 21 element, the contents of six minerals, CEC values (cation exchange capacity) and minerals of less than 20 ȝm in grain size. In addition, multiple regression analyses were conducted, given the correlation between the selected elements and mineralogy[8]. The matrice form is used to express the relationship between the contents of the minerals and elements as the matrice of mineral weight percentages. The contents of the elements and corresponding

minerals are derived by solving the equation. The sum of the formation element weight percentages is equal to 1. The mineralogical constituents and contents of the formation can be determined from the mixed spectrum unfolding of the simulated formation in the sandstone saturated with water. 3.2.2 Identification of reservoir lithology using geochemical element log data The geochemical element capture log can be used to calculate accurately the mineral contents of the formation. Usually, GR and spectral natural gamma ray log data are used as the indicative curve of shale content, but the data of a large number of core analyses indicate that they are not always in good correspondence with the shale content which has a stable relationship with the contents of aluminum and iron elements. The elemental capture spectroscopy (ECS) logging tool is used to measure the captured thermal spectrum of the elements in the formation. As well it is used to obtain, by calculation, some elemental contents of silica, calcium, iron, sulphur, titanium, gadolinium, etc. and the contents of the major minerals in the formation. Its accurate and stable results provide reliable evidence for single-well and multiwell formation evaluation of the formation. The contents of sandstone (QFM), claystone (CLAY) and carbonate rock (CARB) can be calculated from the log presentation (Fig. 2) obtained from the geochemical element log processing and interpretation. The formation of interest in Well Na-xx is the sand-shale zone; the interval above 4730 m is a gypsum claystone. The log of the chemical element ECS can be used to measure effectively the contents of chemical elements and to determine lithology of the formation, incorporating conventional log data. The calcium content in the formation can be effectively determined, but it is difficult to calculate using conventional log data. The calcareous interbed can be taken out and the calcium content in the effective layer can be accurately calculated, based on processed results of ECS. Knowing precisely the lithological constituents is a good basis for an accurate calculation of porosity. The element log results of the formation (Fig. 2) in the well indicate high shaly response values of GR, thorium content, neutron, ECS-measured shale content and the acoustic transit time. The sandstone is characterized mainly by low GR, low thorium content and a small shale content measured by the formation element log, in the vicinity of the sand line on the neutron density cross plots. There is calcareous cementation in part of the interval of the holes, characterized by low GR, elevated resistivity, reduced density/acoustic/neutron porosities and higher shale and calcium contents measured by the formation element log and by its effect on the development of reservoir pores.

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Fig. 2

4

Geochemical element log presentation

Results and discussion

Given the results of the gas-bearing, the porosity calculated from the compensated acoustic and density log data is larger and the porosity calculated from the compensated neutron log data is smaller, but the porosity that is calculated using the Cook method agrees with the analysis of the core porosity. Using the Cook method to calculate the porosity eliminates the skipped cycle caused by gas in the compensated acoustic log which leads to a larger calculated porosity. The gas test results indicate that the calculated porosity from the Cook method can be used for the evaluation of reservoirs[1], which is consistent with our test results. The comparison of the Cook saturation and the saturation calculated using the Archie equation is shown in Fig. 1. We can see from this figure that there is a very small gas saturation calculated for the gas zone using the Archie equation. This is because resistivity in the gas zone is low due to its lithological effect, whereas the calculated gas saturation is relatively accurate using the Cook method. If the porosity are consistent, the permeability calculated using the empirical equation, is far greater than the permeability obtained from the core analysis, but the permeability calculated from the permeability model, covering the shale content, grain size, pore throat texture, etc., is in good agreement with the permeability of the core analysis. When the porosity is high, the permeability calculated using these two methods are consistent, but when the porosity is low

and the shale content is high, the permeability calculated using the empirical equation is in better agreement with the results of the core analysis. For low porosity and permeability, the calculated water saturation is compared with the results from mercury injection, relative permeability and sealed coring. Their relative errors do not exceed 5% at the most. The water saturation in the gas zone of low porosity and permeability is 55%~70%. It is observed that the actual calculation results indicate that the formation constituents, porosity and permeability calculated using the above parameters are in full agreement with the actual results of the core analysis. The absolute error is within the range of –0.01 ~ +0.02 for the permeability obtained from multiple regression analysis and from the rock-electricity experiment. Its relative error is basically less than 0.47, i.e., the permeability error is calculated within a half order of magnitude and consequently the calculated porosity and permeability are credible. For the sandstone of complex lithology, there are less core data available due to cost constraints, but there is a great deal of data available from conventional logs and the logs of captured geochemical elements. For the reliability of the analytical results, it should be stated that the greatest source of errors arises from the results of the conventional logs. In contrast, the results from the coring and logs of the geochemical elements are accurate. The cost of coring is considerable, but the cost of conventional and geochemical element logs is relatively low. The core

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and geochemical element log is more visualized than the conventional log. It also reveals the advantage of new logging technology.

5

Conclusions

The reservoir lithologies in the area are characterized by large variation, high shale contents, high porosity and middle-low permeability and large lithological effect on electrical properties. By utilizing the non-electricity of the Cook method we calculated its saturation, which avoids the large lithological effect and the fluid insensitivity to the electrical properties of the reservoir. On the basis of a proper rock model, the porosity of the reservoir can be accurately calculated. The saturation can be calculated using the non-electricity of the Cook method; the permeability should be calculated by incorporating the characteristics of the reservoir, while considering such factors as shale content, grain size and pore throat texture, which would lead to an accurately calculated permeability. The lack of correlation in the optimized model combination can be used to derive the parameters of the reservoir which are consistent with the rock-electricity experiment. The conventional log can be applied to make a correct identification of gas, oil and water zones in the low porosity and permeability gas-bearing sand reservoir. Different evaluation methods are used for gas-bearing sand reservoirs with different lithologies, physical properties and electric properties, which provide for novel ideas for the study of complex gas-bearing reservoir parameters. Elements such as silicon, calcium, iron, sulphur, titanium, gadolinium, etc., can be obtained from analysis of the relationships between elements and characteristic rays, released from neutron capture and activation with the geochemical element log and between elements and minerals. The elemental capture spectroscopy (ECS) log can effectively measure the contents of chemical elements as well as the calcium contents in the reservoir by combining with conven-

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tional log data, especially for the calcium content, which is difficult to calculate using conventional data. The calcareous interbed can be eliminated and the calcium content in the effective layer can be accurately calculated through the processing of element logs.

Acknowledgements We extend our gratitude to engineer Cheng Xi and Professor Liao Xin-wei, for their useful comments and suggestions for the paper.

References [1]

[2]

[3]

[4]

[5]

[6]

[7] [8]

Dou L M, He X Q. Model for rock burst failure and its critical values of acoustic and electro magnetic emission. Journal of China University of Mining & Technology, 2004, 33(5): 504–508. (In Chinese) Zhang Y J, An L Q, Ren R H, Fan S M, Ma N J, Li J H, Ji Y M. Experimental study of deformation of surrounding rock with infrared radiation. Journal of China University of Mining & Technology, 2005, 5(1): 329–333. Deng Z Y, Zhang D S, An L Q. Experiment of monitoring stress variation of rock using a thermal detection. Journal of China University of Mining & Technology, 2006, 35(5): 623–627. (In Chinese) He Y N, Han L J, Shao P, Jiang B S. Some problems of rock mechanics for roadways stability in depth. Journal of China University of Mining & Technology, 2006, 35(3): 288–295. (In Chinese) Wu L X, Zhong S, Wu Y H, Wang C Y, Zhang L. Time dependent features of thermal infrared radiation temperature of rock impacted by free-falling steel ball. Journal of China University of Mining & Technology, 2005, 34(5): 557–563. (In Chinese) Grau J A, Schweitzer J S. Elemental concentration from thermal neutron captures gamma ray spectra in geological formations. Nuclear Geophysics, 1989(3): 1–9. Herron M M. Mineralogy from geochemical well logging. Clay and Clay Minerals, 1986, 34(2): 204–213. Herron M M, Herron S L. Geological applications of geochemical well logging. Geological Society Special Publication, 1990(48): 165–175.