Application of Machine Learning Algorithms for Petroleum Reservoir Characterization

Chaki, Soumi; Routray, Aurobinda; Mohanty, William K.

doi:10.1002/9781119679998.ch1

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…• DT algorithm is a straightforward, yet effective MLT that creates a flowchart-like model of decisions and their potential outcomes. DTs have been employed in reservoir VOLUME XX, 2023 characterization and property prediction owing to their interpretability and ease of implementation [24].…”

Section: Methodology a Overview Of Machine Learning Techniques Usedmentioning

confidence: 99%

Reservoir Property Prediction in the North Sea Using Machine Learning

Al-Fakih,

Kaka,

Koeshidayatullah

2023

IEEE Access

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The North Sea sedimentary basin is characterized by geological complexity, encompassing a wide range of rock types and structures, including multiple reservoirs (carbonates and siliciclastic) with variations in reservoir quality and heterogeneity. These phenomena pose significant challenges for accurately predicting reservoir properties using traditional well log analysis. Moreover, these challenges are further compounded by complex geological conditions and scarcity of available data. Hence, the aim of this study was to address these challenges by applying advanced machine learning techniques within this basin. This study delves into both supervised and unsupervised machine learning approaches to forecast the essential petrophysical properties that are crucial for assessing reservoir quality. These properties encompass total porosity, effective porosity, and shale volume, all derived from well log data originating from the North Sea sedimentary basin. The models were trained using data from four wells consisting of 32,215 data points (80% for training, 10% for testing, and 10% for validation). Furthermore, our study introduced pioneering datadriven preprocessing workflow, encompassing exploratory data analysis, missing data imputation, and outlier detection to improve the performance of the machine learning models. ANN and RF models achieved the best results among the algorithms evaluated, with an average MAE of 0.01, RMSE of 0.01, and R-squared of 0.95 for total porosity, effective porosity, and volume of shale, respectively. These metrics demonstrate that the model can accurately predict reservoir properties in a challenging sedimentary basin, even with limited data availability, enabling reservoir characteristics and field development optimization, particularly in areas where core data are scarce.

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Section: Methodology a Overview Of Machine Learning Techniques Usedmentioning

confidence: 99%

Reservoir Property Prediction in the North Sea Using Machine Learning

Al-Fakih,

Kaka,

Koeshidayatullah

2023

IEEE Access

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“…Machine learning algorithms can be employed for reservoir characterization, production forecasting, anomaly detection, and predictive maintenance, among others. ML techniques are used in various applications for the exploration and production of hydrocarbons, for instance, in reservoir characterizations [12,13], production design [14], well completion [15], and drilling engineering [16]. A comprehensive review of the applications of artificial intelligence techniques in the context of petroleum engineering can be found in [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

New Insights into Fracture Porosity Estimations Using Machine Learning and Advanced Logging Tools

Ifrene,

Irofti,

et al. 2023

Fuels

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Fracture porosity is crucial for storage and production efficiency in fractured tight reservoirs. Geophysical image logs using resistivity measurements have traditionally been used for fracture characterization. This study aims to develop a novel, hybrid machine-learning method to predict fracture porosity using conventional well logs in the Ahnet field, Algeria. Initially, we explored an Artificial Neural Network (ANN) model for regression analysis. To overcome the limitations of ANN, we proposed a hybrid model combining Support Vector Machine (SVM) classification and ANN regression, resulting in improved fracture porosity predictions. The models were tested against logging data by combining the Machine Learning approach with advanced logging tools recorded in two wells. In this context, we used electrical image logs and the dipole acoustic tool, which allowed us to identify 404 open fractures and 231 closed fractures and, consequently, to assess the fracture porosity. The results were then fed into two machine-learning algorithms. Pure Artificial Neural Networks and hybrid models were used to obtain comprehensive results, which were subsequently tested to check the accuracy of the models. The outputs obtained from the two methods demonstrate that the hybridized model has a lower Root Mean Square Error (RMSE) than pure ANN. The results of our approach strongly suggest that incorporating hybridized machine learning algorithms into fracture porosity estimations can contribute to the development of more trustworthy static reservoir models in simulation programs. Finally, the combination of Machine Learning (ML) and well log analysis made it possible to reliably estimate fracture porosity in the Ahnet field in Algeria, where, in many places, advanced logging data are absent or expensive.

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New Insights in Fracture Porosity Estimation using Machine Learning and Advanced Logging Tools

Ifrene,

Irofti,

et al. 2023

Preprint

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The purpose of this work is to compare two fracture prediction models with real-world data. The pure Artificial Neural Network (ANN) model emphasizes regression analysis, while the hybrid model (SVM-ANN) focuses on the combination of regression and classification analysis or Support Vector Machine. The results were subsequently tested against logging data by combining the Machine Learning approach with advanced logging tools. In this context, we used electrical image logs and the dipole acoustic tool which together allowed the distinction of 404 open fractures and 231 closed fractures and, consequently the estimation of fracture porosity. The results are then fed into two machine-learning algorithms. Pure Artificial Neural Networks and hybrid models are used to establish comprehensive results, which are subsequently tested to check the accuracy of the models. The outputs obtained from the two methods demonstrate that the hybridized model has a lower Root Mean Square Error (RMSE) than pure ANN. The results of our approach strongly suggest that incorporating hybridized machine learning algorithms in fracture porosity estimations can contribute to the development of more trustworthy static reservoir models in simulation programs. Finally, the combination of Machine Learning (ML) and well-log analysis do permit reliable estimation of fracture porosity in the Ahnet field in Algeria, where, in many places, advanced logging data is absent and costly.

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Application of Machine Learning Algorithms for Petroleum Reservoir Characterization

Cited by 3 publications

References 43 publications

Reservoir Property Prediction in the North Sea Using Machine Learning

Reservoir Property Prediction in the North Sea Using Machine Learning

New Insights into Fracture Porosity Estimations Using Machine Learning and Advanced Logging Tools

New Insights in Fracture Porosity Estimation using Machine Learning and Advanced Logging Tools

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