Machine learning technique for the quantitative evaluation of tight sandstone reservoirs using high-pressure mercury-injection data

2020 ◽  
Author(s):  
JingJing Liu ◽  
JianChao Liu
Keyword(s):  
Machine Learning ◽  
High Pressure ◽  
Quantitative Evaluation ◽  
Oil And Gas ◽  
Tight Sandstone ◽  
Reservoir Evaluation ◽  
Oil And Gas Exploration ◽  
Unconventional Oil ◽  
Unconventional Oil And Gas ◽  
Sandstone Reservoirs

<p>In recent years, China's unconventional oil and gas exploration and development has developed rapidly and has entered a strategic breakthrough period. At the same time, tight sandstone reservoirs have become a highlight of unconventional oil and gas development in the Ordos Basin in China due to its industrial and strategic value. As a digital representation of storage capacity, reservoir evaluation is a vital component of tight-oil exploration and development. Previous work on reservoir evaluation indicated that achieving satisfactory results is difficult because of reservoir heterogeneity and considerable risk of subjective or technical errors. In the data-driven era, this paper proposes a machine learning quantitative evaluation method for tight sandstone reservoirs based on K-means and random forests using high-pressure mercury-injection data. This method can not only provide new ideas for reservoir evaluation, but also be used for prediction and evaluation of other aspects in the field of oil and gas exploration and production, and then provide a more comprehensive parameter basis for “intelligent oil fields”. The results show that the reservoirs could be divided into three types, and the quantitative reservoir-evaluation criteria were established. This method has strong applicability, evident reservoir characteristics, and observable discrimination. The implications of these findings regarding ultra-low permeability and complex pore structures are practical.</p>

Classification of Tight Sandstone Reservoir Based on the Conventional Logging

2014 ◽  
Vol 633-634 ◽  
pp. 526-529 ◽  
Author(s):  
Xiao Ling Xiao ◽  
Jia Li Cui ◽  
Yu Peng Zhang ◽  
Xiang Zhang ◽  
Han Wu
Keyword(s):  
Support Vector Machine ◽  
Oil And Gas ◽  
Identification Accuracy ◽  
Support Vector ◽  
Tight Sandstone ◽  
Oil And Gas Exploration ◽  
Unconventional Oil ◽  
Unconventional Oil And Gas ◽  
Sandstone Reservoir

With the increasing social demand for oil and gas resources, the exploration and development of unconventional oil and gas reservoirs will pay more and more attention. Tight sandstone reservoir classification is one of the important tasks in the research of unconventional oil and gas exploration and development.Limitations exist in tight sandstone reservoir classification by various conventional logging.A method for the classification of tight sandstone reservoir based on support vector machine is presented in this paper, combining with the core data and flow unit to establish the reservoir classification standard. Tight sandstone reservoirs of no coring wells are classified based on the model made by support vector machine using conventional logging.The application results show that this method has high suitability and identification accuracy.

Application of machine learning algorithms to predict permeability in tight sandstone formations

Nafta-Gaz ◽  
2021 ◽  
Vol 77 (5) ◽  
pp. 283-292
Author(s):  
Tomasz Topór ◽  
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Learning Algorithms ◽  
Confining Pressure ◽  
Machine Learning Algorithms ◽  
Core Material ◽  
Confining Stress ◽  
Tight Sandstone ◽  
Oil And Gas Exploration ◽  
Better Than

The application of machine learning algorithms in petroleum geology has opened a new chapter in oil and gas exploration. Machine learning algorithms have been successfully used to predict crucial petrophysical properties when characterizing reservoirs. This study utilizes the concept of machine learning to predict permeability under confining stress conditions for samples from tight sandstone formations. The models were constructed using two machine learning algorithms of varying complexity (multiple linear regression [MLR] and random forests [RF]) and trained on a dataset that combined basic well information, basic petrophysical data, and rock type from a visual inspection of the core material. The RF algorithm underwent feature engineering to increase the number of predictors in the models. In order to check the training models’ robustness, 10-fold cross-validation was performed. The MLR and RF applications demonstrated that both algorithms can accurately predict permeability under constant confining pressure (R2 0.800 vs. 0.834). The RF accuracy was about 3% better than that of the MLR and about 6% better than the linear reference regression (LR) that utilized only porosity. Porosity was the most influential feature of the models’ performance. In the case of RF, the depth was also significant in the permeability predictions, which could be evidence of hidden interactions between the variables of porosity and depth. The local interpretation revealed the common features among outliers. Both the training and testing sets had moderate-low porosity (3–10%) and a lack of fractures. In the test set, calcite or quartz cementation also led to poor permeability predictions. The workflow that utilizes the tidymodels concept will be further applied in more complex examples to predict spatial petrophysical features from seismic attributes using various machine learning algorithms.

scholarly journals Characteristics and evaluation of the Upper Paleozoic source rocks in the Southern North China Basin

2021 ◽  
Vol 13 (1) ◽  
pp. 294-309
Author(s):  
Fengyu Sun ◽  
Gaoshe Cao ◽  
Zhou Xing ◽  
Shuangjie Yu ◽  
Bangbang Fang
Keyword(s):  
Source Rock ◽  
North China ◽  
Oil And Gas ◽  
Source Rocks ◽  
Oil And Gas Exploration ◽  
Potential Source ◽  
Unconventional Oil ◽  
Upper Paleozoic ◽  
Unconventional Oil And Gas ◽  
North China Basin

Abstract The Upper Paleozoic coal measure strata in the Southern North China Basin have good potential for unconventional oil and gas exploration. However, there has been no systematic evaluation of potential source rock in this area; this affects the estimation of potential resources and the choice of exploratory target layers. In this study, full core holes ZK0901 and ZK0401, which perfectly reveal Upper Paleozoic strata in the study area, systematically collected and analyzed the samples for total organic carbon, rock pyrolysis, chloroform bitumen “A,” organic maceral, vitrinite reflectance, and kerogen carbon isotopes. The results showed that in addition to coal rocks, mudstones and carbonate rocks are also potential source rocks in the Upper Paleozoic strata. Vertically, the source rocks are continuous in Taiyuan Formation, the lower part of Shanxi Formation, and Lower Shihezi Formation. The organic matter type in the Upper Paleozoic coal rocks and mudstone source rock belong to type III or II. This phenomenon is mainly attributed to the special transgressive–regressive sedimentary environment of the carbonate rocks. The higher degree of thermal evolution in the Upper Paleozoic source rocks may be related to the structure or a higher paleogeothermal gradient in this area. The coal layer and its upper and lower mudstone of the Shanxi Formation and Lower Shihezi Formation are the main target layers of unconventional oil and gas exploration. The results from this study can be used as a reference for the study on potential source rock for unconventional oil and gas exploration in the Southern North China Basin.

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