Analysis of low-resistivity oil pay and fluid typing method of Chang 81 Member, Yanchang Formation in Huanxian area, Ordos Basin, China

Bai, Ze; Tan, Maojin; Li, Gao‐Ren; Shi, Yuqing

doi:10.1016/j.petrol.2019.01.015

Cited by 32 publications

(10 citation statements)

References 16 publications

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“…There are many reasons for low resistivity in oil and gas reservoirs, such as the development of conductive minerals (e.g., pyrite and magnetite), a high level of mineralization of the formation water, the presence of additional conductive clays, and the influence of the surrounding rocks. 26 In addition, geological factors such as the geological structure, sedimentary environment, and sedimentary facies belt may also affect the formation of low-resistivity reservoirs. 24 In many cases, the condition of low resistivity is not caused by a single factor but may be the result of multiple factors acting together.…”

Section: Analysis Of the Origin Of Low Resistivity Inmentioning

confidence: 99%

“…Based on the electrical characteristics, low resistivity in conventional oil formations can be divided into two types: absolute low resistivity, in which the absolute resistivity is <5 Ω·m, , and relative low resistivity, in which the ratio of the resistivity increase in the oil layers in the same oil–water system is <3 or even <2. − The “low resistivity” of shale gas reservoirs discussed in this paper refers to a scenario in which the resistivity of the reservoir or reservoir part is less than that of the nongas shale in the same or adjacent layer due to the conductivity of the formation mineral matrix. , Sun et al established a parallel saturation model for the scenario of low resistivity caused by pyrite development. The model reveals that the resistivity of the shale gas reservoir is mainly affected by the pyrite, organic matter, clay, and formation water in micropores and microfractures; the resistivity of each component can be calculated and then connected in parallel to establish a relevant saturation model.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of the Origin of Low Resistivity and Methods for the Calculation of Gas Saturation in Shale Gas Reservoirs in the Fuling Area

et al. 2021

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To address the challenge of inaccurate gas saturation values calculated by using Archie-type resistivity model as a result of the low resistivity of shale gas reservoirs in the Wufeng−Longmaxi Formation in the Fuling area, two nonresistivity gas saturation models were established, in this study, based on the relationships between the gas saturation and dipole array acoustic/conventional logging parameters as well as gas saturation and total organic carbon (TOC). The feasibility and accuracy of the proposed methods were verified with example wells. The results indicate that the main cause of the low resistivity in shale gas reservoirs in the Fuling area is shale graphitization; however, multiple factors such as the development of pyrite and the additional conductivity of clay also contribute. The S−P wave slowness ratio (RMSC) of shale gas reservoirs in the study area decreases significantly with increasing gas saturation and has a good correlation with the density (DEN). Thus, a saturation calculation model could be established based on the RMSC and DEN and the gas saturation from core analysis. The gas saturation in shale gas reservoirs increases logarithmically with increasing TOC with a good correlation. Therefore, a saturation calculation model could be established based on measured TOC values. The calculation accuracy of the two models is higher than that of the Archie-type resistivity model. This study shows that the saturation model established by fitting the RMSC and DEN is suitable for the case of core analysis with fair regional applicability. The TOC-based saturation model has good universality. Therefore, these two new models are suitable for gas saturation calculations in low-resistivity shale gas reservoirs and have the potential for wider applications.

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Section: Analysis Of the Origin Of Low Resistivity Inmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of the Origin of Low Resistivity and Methods for the Calculation of Gas Saturation in Shale Gas Reservoirs in the Fuling Area

et al. 2021

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“…At present, low porosity and low permeability reservoirs represented by tight sandstone has become the main battlefield to ensure the supply of oil and gas resources 5 . However, the complex pore structure and strong heterogeneity of tight sandstone reservoir reduce the sensitivity of the logging response to pore fluid, resulting in more resistivity low-contrast oil pays developed, and it is more difficult to interpret and identify this kind of reservoir by using conventional logging interpretation methods 6 , 7 , 25 .…”

Section: Introductionmentioning

confidence: 99%

Log interpretation method of resistivity low-contrast oil pays in Chang 8 tight sandstone of Huanxian area, Ordos Basin by support vector machine

Bai

Tan

Shi

et al. 2022

Sci Rep

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Resistivity low-contrast oil pays are a kind of unconventional oil resource with no obvious difference in physical and electrical properties from water layers, which makes it difficult to be identified based on the characteristics of the geophysical well logging response. In this study, the support vector machine (SVM) technology was used to interpret the resistivity low-contrast oil pays in Chang 8 tight sandstone reservoir of Huanxian area, Ordos Basin. First, the input data sequences of logging curves were selected by analyzing the relationship between reservoir fluid types and logging data. Then, the SVM classification model for fluid identification and SVR regression model for reservoir parameter prediction were constructed. Finally, these two models were applied to interpret the resistivity low-contrast oil pays in the study area. The application results show that the fluid recognition accuracy of the SVM classification model is higher than that of the logging cross plot method, back propagation neural network method and radial basis function neural network method. The calculation accuracy of permeability and water saturation predicted by the SVR regression model is higher than that based on the experimental fitting model, which indicates that it is feasible to carry out logging interpretation and evaluation of the resistivity low-contrast oil pays by the SVM method. The research results not only provide an important reference and basis for the review of old wells but also provide technical support for the exploration and development of new strata.

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“…Estimation of petrophysical properties such as porosity, permeability, the volume of shale, fluid saturation and thickness of reservoirs plays a vital role in the oil and gas industry (Kumar et al 2016;Saadu and Nwankwo 2017). Well-logging data can accurately detect reservoir fluid volume and composition, if interpreted correctly and ideally calibrated to core data (Asquith et al 2004;Darling 2005;Abudeif et al 2016;Radwan 2018;Bai et al 2019). Gas-bearing zones will show a greater separation in neutron-density crossover than oil zones, and oil can be inferred to be in places where low crossover magnitude is detected (Asquith et al 2004;Schechter 2010).…”

Section: Introductionmentioning

confidence: 99%

Sandstone reservoir characteristics of Rio Del Rey basin, Cameroon, using well-logging analysis

Kana

Ahmad

Gouet

et al. 2021

J Petrol Explor Prod Technol

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The present work deals with an interpretation of well log data (gamma ray (GR), resistivity, density, and neutron) from four wells, namely P-1, P-2, P-3 and P-4 in the study area of the Rio Del Rey basin. The well logs analysis indicates five potential sandstone reservoirs at the P-1, two at the P-2, four at the P-3 and six at the P-4. The neutron–density-GR logs highlight the sandstone gas reservoir characterized by high resistivity and crossover between neutron density. The neutron–density-GR cross-plot confirms the presence of sandstone containing hydrocarbons by a displacement of the cloud of points, from low to medium GR values, from the sandstone line to the left. Petrophysical parameters exhibit the value 12–41% for a volume of shale, 15–34% for effective porosity, 29–278 mD for permeability and 3–63% for water saturation. The three potential hydrocarbon reservoir saturation ranges from 22 to 45%. The study will contribute to future offshore oil and gas exploration and development in the Rio Del Rey basin, based on the geological and geophysical characteristics of the reservoirs delineated.

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Analysis of low-resistivity oil pay and fluid typing method of Chang 81 Member, Yanchang Formation in Huanxian area, Ordos Basin, China

Cited by 32 publications

References 16 publications

Investigation of the Origin of Low Resistivity and Methods for the Calculation of Gas Saturation in Shale Gas Reservoirs in the Fuling Area

Investigation of the Origin of Low Resistivity and Methods for the Calculation of Gas Saturation in Shale Gas Reservoirs in the Fuling Area

Log interpretation method of resistivity low-contrast oil pays in Chang 8 tight sandstone of Huanxian area, Ordos Basin by support vector machine

Sandstone reservoir characteristics of Rio Del Rey basin, Cameroon, using well-logging analysis

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