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

Bai, Ze; Tan, Maojin; Shi, Yuqing; Guan, Xingning; Wu, Haibo; Huang, Yonghui

doi:10.1038/s41598-022-04962-0

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…Irreducible water saturation (S wirr ) is an important parameter in evaluation of low permeability sandstone reservoirs and identification of pore fluids, because high S wirr is a key factor that causes contrast of low resistivity in such type of reservoirs [3]. Generally, S wirr is calculated from NMR logging by using a T 2cutoff .…”

Section: Estimation Of Irreducible Water Saturation (S Wirr )mentioning

confidence: 99%

“…Tight reservoirs became the main battlefield to ensure oil and gas resources supplements [2]. Low permeability to tight sandstone reservoirs accounted for more than 60% of the newly added oil reserves in recent decades in China [3].…”

Section: Introductionmentioning

confidence: 99%

“…Processes 2023, 11, x FOR PEER REVIEW 12 of 26ρf are the bulk density of rock matrix and pore fluid, respectively. The units are is g/cm3 ; CNL is the neutron logging value; and CNLclay is the neutron logging value in clay point; the unit is v/v. Combining with φD, φN, φDclay and φNclay, low permeability sandstone reservoir porosity was calculated based on the triangular chart:…”

mentioning

confidence: 99%

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Low Permeability Gas-Bearing Sandstone Reservoirs Characterization from Geophysical Well Logging Data: A Case Study of Pinghu Formation in KQT Region, East China Sea

Gao

Xiao

Zhang³

et al. 2023

Processes

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The Pinghu Formation is a low permeability sandstone reservoir in the KQT Region, East China Sea. Its porosity ranges from 3.6 to 18.0%, and permeability is distributed from 0.5 to 251.19 mD. The relationship between porosity and permeability was poor due to strong heterogeneity. This led to the difficulty of quantitatively evaluating effective reservoirs and identifying pore fluids by using common methods. In this study, to effectively evaluate low permeability sandstones in the Pinghu Formation of KQT Region, pore structure was first characterized from nuclear magnetic resonance (NMR) logging based on piecewise function calibration (PFC) method. Effective formation classification criteria were established to indicate the “sweet spot”. Afterwards, several effective methods were proposed to calculate formation of petrophysical parameters, e.g., porosity, permeability, water saturation (Sw), irreducible water saturation (Swirr). Finally, two techniques, established based on the crossplots of mean value of apparent formation water resistivity (Rwam) versus variance of apparent formation water resistivity (Rwav)—Sw versus Swirr—were adopted to distinguish hydrocarbon-bearing formations from water saturated layers. Field applications in two different regions illustrated that the established methods and techniques were widely applicable. Computed petrophysical parameters matched well with core-derived results, and pore fluids were obviously identified. These methods were valuable in improving low permeability sandstone reservoirs characterization.

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Section: Estimation Of Irreducible Water Saturation (S Wirr )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Low Permeability Gas-Bearing Sandstone Reservoirs Characterization from Geophysical Well Logging Data: A Case Study of Pinghu Formation in KQT Region, East China Sea

Gao

Xiao

Zhang³

et al. 2023

Processes

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“…Found in most parts of the world, tight oil and gas reservoirs have become a focus of petroleum and natural gas engineers, and their exploration and development has affected the world's energy supply and demand patterns. Tight oil and gas reservoirs in China have become an important resource for unconventional oil and gas to increase reserves and production, and are widely distributed in China's Ordos Basin [4], Sichuan Basin [5,6], Tarim Basin [7], and Qaidam Basin [8]. Tight reservoirs are characterized by low porosity, low permeability, and strong inhomogeneity, and fluid identification plays a key role in the formulation and adjustment of field development plans.…”

Section: Introductionmentioning

confidence: 99%

Hybrid parameters for fluid identification using an improved quantum neural network in a tight reservoir

Luo

Liu

Yang

et al. 2023

Preprint

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This paper proposes a fluid classifier for a tight reservoir using a quantum neural network (QNN). It is difficult to identify the fluid in tight reservoirs, and the manual interpretation of logging data, which is an important means to identify the fluid properties, has the disadvantages of a low recognition rate and non-intelligence, and an intelligent algorithm can better identify the fluid. For tight reservoirs, the logging response characteristics of different fluid properties and the sensitivity and relevance of well log parameter and rock physics parameters to fluid identification are analyzed, and different sets of input parameters for fluid identification are constructed. On the basis of quantum neural networks, a new method for combining sample quantum state descriptions, sensitivity analysis of input parameters, and wavelet activation functions for optimization is proposed. The results of identifying the dry layer, gas layer, and gas-water co-layer in the tight reservoir in the Sichuan Basin of China show that different input parameters and activation functions affect recognition performance. The proposed quantum neural network based on hybrid parameters and a wavelet activation function has higher fluid identification accuracy than the original quantum neural network model, indicating that this method is effective and warrants promotion and application.

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“…In the past few years, a noticeable surge of machine learning applications in the petrophysical well-logging has been seen, ranging from improved interpretation of logs 11 and forecasting of stress in horizontal wells 12 to the utilization of neural networks to synthesize the artificial NMR T 2 logs 13 . In this study, two machine learning techniques are employed to improve the water content determination in oil-sands, using as inputs the T 2 relaxation and bulk density data, along with Extreme Gradient Boosting (XGBoost) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Application of XGBoost model for in-situ water saturation determination in Canadian oil-sands by LF-NMR and density data

Markovic

Bryan²,

Rezaee

et al. 2022

Sci Rep

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Water saturation determination is among the most challenging tasks in petrophysical well-logging, which directly impacts the decision-making process in hydrocarbon exploration and production. Low-field nuclear magnetic resonance (LF-NMR) measurements can provide reliable evaluation. However, quantification of oil and water volumes is problematic when their NMR signals are not distinct. To overcome this, we developed two machine learning frameworks for predicting relative water content in oil-sand samples using LF-NMR spin–spin (T2) relaxation and bulk density data to derive a model based on Extreme Gradient Boosting. The first one facilitates feature engineering based on empirical knowledge from the T2 relaxation distribution analysis domain and mutual information feature extraction technique, while the second model considers whole samples’ NMR T2-relaxation distribution. The NMR T2 distributions were obtained for 82 Canadian oil-sands samples at ambient and reservoir temperatures (164 data points). The true water content was determined by Dean-Stark extraction. The statistical scores confirm the strong generalization ability of the feature engineering LF-NMR model in predicting relative water content by Dean-Stark—root-mean-square error of 0.67% and mean-absolute error of 0.53% (R2 = 0.90). Results indicate that this approach can be extended for the improved in-situ water saturation evaluation by LF-NMR and bulk density measurements.

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

Cited by 7 publications

References 21 publications

Low Permeability Gas-Bearing Sandstone Reservoirs Characterization from Geophysical Well Logging Data: A Case Study of Pinghu Formation in KQT Region, East China Sea

Low Permeability Gas-Bearing Sandstone Reservoirs Characterization from Geophysical Well Logging Data: A Case Study of Pinghu Formation in KQT Region, East China Sea

Hybrid parameters for fluid identification using an improved quantum neural network in a tight reservoir

Application of XGBoost model for in-situ water saturation determination in Canadian oil-sands by LF-NMR and density data

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