An integrated workflow to characterize and evaluate low resistivity pay and its phenomenon in a sandstone reservoir

Pratama, Edo; Ismail, Mokhtar Che; Ridha, Syahrir

doi:10.1088/1742-2140/aa5efb

Cited by 23 publications

(7 citation statements)

References 7 publications

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“…Fine lithology, complicated pore structure, high content of irreducible water, high salinity of formation water, additional conductivity of clay, low amplitude structures, drilling fluid invasion and super-hydrophilic of rocks are all factors that lead to low-resistivity oil pays. [9][10][11] Nowadays， Scholars have made a lot of contributions to the identification of low-resistivity oil pays, many fluid identification methods have been proposed for typing low-resistivity oil pay, such as overlap method, cross-plot method, nuclear magnetic resonance (NMR) logging method. Quantitative calculations include the early Archie formula, 12 the RRSR method, the improved PICKETT method, classical ways such as Simandoux and the Indonesian equation.…”

Section: Introductionmentioning

confidence: 99%

Genetic mechanisms and multi-parameter logging identification of low-resistivity oil pay: A case study of the Triassic Chang 6 member, Zhidan area, Ordos Basin, China

Wang

Zhu

Zhang³

2023

Preprint

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Low-resistivity pay have been found throughout the world, the identification and characterization of low-resistivity pay is very challenging and important for the development of oil fields. The genesis of the low-resistivity oil pay is complex, and the logging response characteristics are variable. The weak difference in resistivity between the oil pay and the adjacent water pay makes it difficult to identify kinds of fluids by resistivity log analysis, which reduces the overall exploration benefit of the oilfield. Therefore, it is very important to study the genesis and identification technology of the low-resistivity oil pay. In this paper, we first analyzed the core experimental results such as X-ray diffraction scanning electron microscope, mercury intrusion, phase permeability, nuclear magnetic resonance, physical properties, electric petrophysical experiment, micro-CT technology and rock wettability, etc. Comprehensive analysis of the reservoir characteristics shows that the development of low-resistivity oil pays in the study area is controlled by irreducible water saturation and high gamma ray sandstone. The complicated pore structure and rock hydrophilicity are the factors that lead to the increase of irreducible water saturation. Then, the salinity of formation water and the invasion of drilling fluid also have a certain influence on the change of reservoir resistivity. According to the controlling factors of the low- resistivity oil pay, we extract the sensitive parameters to the logging response, amplify the difference between oil and water pay, and use the AC-RILD, SP-PSP, GR*GR*∆SP-RILD and(RILM-RILD)/RILD—RILD cross-plots, etc. Various methods such as cross-plots method, overlap method and movable water analysis are mutually constrained to identify low-resistivity oil pays. In the case study, the comprehensive application of the above identification flow path can effectively improve the accuracy of fluid recognition step by step. It provides reference for identifying more low-resistivity reservoirs with similar geological conditions.

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Section: Introductionmentioning

confidence: 99%

Genetic mechanisms and multi-parameter logging identification of low-resistivity oil pay: A case study of the Triassic Chang 6 member, Zhidan area, Ordos Basin, China

Wang

Zhu

Zhang³

2023

Preprint

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“…With the increasing volatility of international oil prices and the continuous reduction of oil reservoir scale, the resistivity low-contrast oil resources with strong concealment has received much interest in recent years. Carrying out the research on logging interpretation and evaluation method of resistivity low-contrast oil pays has become the most practical choice to supplement conventional oil resources and reduce oilfield exploration cost 1 , 2 , 27 . The resistivity low-contrast oil pay has the characteristics of little difference in porosity and resistivity logging response from water layer, and the oil saturation of resistivity low-contrast oil layer is relatively low 3 , 4 .…”

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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“…However, there is a law to follow for the resistivity low-contrast oil pay in a certain area or a certain type of specific reservoir (Yan et al 2010;Mao 2016). On the basis of reservoir characteristics analysis, studying the main influence factors of resistivity low-contrast oil pay from multiple aspects is the key to carry out effective log interpretation and evaluation (Mao et al 2007;Ayham et al 2016;Pratama et al 2017). Kusuma et al (2016) proposed an effective method to identify resistivity low-contrast oil pay zones during the re-evaluation of old wells in the "S" field by integrated analysis of the main factors causing the low resistivity zones.…”

Section: Introductionmentioning

confidence: 99%

Reservoir characteristics and control mechanism of resistivity low-contrast oil pays in Chang 8 tight sandstone of Longdong West area, Ordos Basin

Bai

Tan

Shi

et al. 2021

J Petrol Explor Prod Technol

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Log interpretation and evaluation of tight sandstone reservoir in Chang 8 Member of Longdong West area, Ordos Basin, China, are facing great challenges due to the co-development of normal oil pay and resistivity low-contrast oil pay. To better guide the exploration and development of oil resources in this area, the reservoir characteristics and control mechanism of resistivity low-contrast oil pay were studied. Firstly, the reservoirs were divided into resistivity low-contrast oil pay (RLP) and normal oil pay (NP) based on the relative value of the apparent resistivity increase rate. Then, the difference of reservoir characteristics between RLP and NP is analyzed by comparing a series of experimental data and real logging data in those two reservoir types. Finally, the control mechanism of RLP was studied from reservoir micro-factors and regional macro-factors, respectively. It is found that the chlorite and illite are the most abundant clay minerals in RLP and NP, respectively. Compared with NP reservoir, the average porosity of RLP is better, but the pore space is mainly composed of micropores, which lead to smaller average pore throat radius and poor pore structure. The high irreducible water saturation and high formation water salinity reduced the reservoir resistivity from micro-aspect. Besides, the difference of hydrocarbon expulsion capacity of source rock and the regional difference of formation water salinity controlled the distribution of RLP and NP. Comprehensive consideration of the reservoir micro-factors and regional macro-factors is important to carry out effective logging interpretation and evaluation.

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An integrated workflow to characterize and evaluate low resistivity pay and its phenomenon in a sandstone reservoir

Cited by 23 publications

References 7 publications

Genetic mechanisms and multi-parameter logging identification of low-resistivity oil pay: A case study of the Triassic Chang 6 member, Zhidan area, Ordos Basin, China

Genetic mechanisms and multi-parameter logging identification of low-resistivity oil pay: A case study of the Triassic Chang 6 member, Zhidan area, Ordos Basin, China

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

Reservoir characteristics and control mechanism of resistivity low-contrast oil pays in Chang 8 tight sandstone of Longdong West area, Ordos Basin

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