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Application of Machine Learning in Rock Characterization

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H: Geo-Energy".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 18353

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Special Issue Editors

Prof. Dr. Reza Rezaee

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Guest Editor

Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Kent St, Bentley WA 6102, Australia
Interests: formation evaluation; petrophysics; unconventional gas (tight gas sand and shale gas); reservoir characterization and modeling
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ali Kadkhodaie

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Guest Editor

Department of Erath Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, 29 Bahman Boulevard, Iran
Interests: formation evaluation and reservoir characterization

Special Issue Information

Dear Colleagues,

Nowadays, with the increasing availability of cost-effective and efficient computing power, machine learning (ML) and artificial intelligence (AI) techniques are becoming increasingly used to replace or augment traditional workflows in several industries. ML are computer programs that can be trained to perform assigned tasks or to make decisions which enable computer systems to learn patterns from data observations.

Although there are many related published examples on the application of ML in rock and fluid characterization, continuous progress, and newly evolved ensemble methods, helped to introduce this Special Issue to collect works that highlight the recent advances in ML application for rock characterization.

This Special issue accepts original articles that use conventional well logs and ML techniques in the following subjects:

Synthesizing missing well logs;
Prediction of petrophysical properties such as porosity, permeability, shear wave velocity, capillary pressure, rock composition, etc;
Prediction of Geochemical properties such as total organic carbon (TOC) content, Production Index, thermal maturity, Langmuir isotherm parameters, etc;
Prediction of Geomechanical properties, such as rock strength, Young’s modulus, Poisson’s ratio, pore pressure, etc;
Well log analysis using ML;
Rock typing and facies classification;
Prediction of lithology from drilling data;
Prediction of fracture parameters such as fracture density and fracture aperture;
Identification of sweet spots in unconventional reservoirs.

Prof. Dr. Reza Rezaee
Prof. Dr. Ali Kadkhodaie
Guest Editors

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Published Papers (5 papers)

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Research

19 pages, 8017 KiB

Open AccessArticle

Artificial Intelligence Model in Predicting Geomechanical Properties for Shale Formation: A Field Case in Permian Basin

by Fatick Nath, Sarker Monojit Asish, Deepak Ganta, Happy Rani Debi, Gabriel Aguirre and Edgardo Aguirre

Energies 2022, 15(22), 8752; https://doi.org/10.3390/en15228752 - 21 Nov 2022

Cited by 5 | Viewed by 2662

Abstract

Due to complexities in geologic structure, heterogeneity, and insufficient borehole information, shale formation faces challenges in accurately estimating the elastic properties of rock which triggers severe technical challenges in safe drilling and completion. These geomechanical properties could be computed from acoustic logs, however, accurate estimation is critical due to log deficit and a higher recovery expense of inadequate datasets. To fill the gap, this study focuses on predicting the sonic properties of rock using deep neural network (Bi-directional long short-time memory, Bi-LSTM) and random forest (RF) algorithms to estimate and evaluate the geomechanical properties of the potential unconventional formation, Permian Basin, situated in West Texas. A total of three wells were examined using both single-well and cross-well prediction algorithms. Log-derived single-well prediction models include a 75:25 ratio for training and testing the data whereas the cross-well includes two wells for training and the remaining well was used for testing. The selected well input logs include compressional wave slowness, resistivity, gamma-ray, porosity, and bulk density to predict shear wave slowness. The results using RF and Bi-LSTM show a promising prediction of geomechanical properties for Permian Basin wells. RF algorithm performed superior for both single and grouped well prediction. The single-well prediction method using the RF algorithm provided the highest accuracy of 99.90% whereas Bi-LSTM gave 93.60%. The best accuracy for a grouped well prediction was achieved employing Bi-LSTM and RF models, i.e., 96.01% and 93.80%. The average prediction including RF and Bi-LSTM algorithms demonstrated that accuracy for single well and cross well prediction is 96% and 94% respectively with an error below 7%. These outcomes show the astonishing capability of artificial intelligence (AI) models trained to create a realistic prediction to unlock unconventional potential when datasets are inadequate. Given adequate training data, operators could leverage these efficient tools by utilizing them to examine fracture interpretations with reduced cost and time when datasets are incomplete and thus increase the hydrocarbon recovery potential. Full article

(This article belongs to the Special Issue Application of Machine Learning in Rock Characterization)

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20 pages, 10403 KiB

Open AccessArticle

Deep-XFCT: Deep Learning 3D-Mineral Liberation Analysis with Micro-X-ray Fluorescence and Computed Tomography

by Patrick Kin Man Tung, Amalia Yunita Halim, Huixin Wang, Anne Rich, Christopher Marjo and Klaus Regenauer-Lieb

Energies 2022, 15(15), 5326; https://doi.org/10.3390/en15155326 - 22 Jul 2022

Cited by 4 | Viewed by 2651

Abstract

Quantitative characterisation through mineral liberation analysis is required for effective minerals processing in areas such as mineral deposits, tailings and reservoirs in industries for resources, environment and materials science. Current practices in mineral liberation analysis are based on 2D representations, leading to systematic errors in the extrapolation to 3D volumetric properties. The rapid development of X-ray microcomputed tomography (μCT) opens new opportunities for 3D analysis of features such as particle- and grain-size characterisation, determination of particle densities and shape factors, estimation of mineral associations, and liberation and locking. To date, no simple non-destructive method exists for 3D mineral liberation analysis. We present a new development based on combining μCT with micro-X-ray fluorescence (μXRF) using deep learning. We demonstrate successful semi-automated multimodal analysis of a crystalline magmatic rock by obtaining 2D μXRF mineral maps from the top and bottom of the cylindrical core and propagating that information through the 3D μCT volume with deep learning segmentation. The deep learning model was able to segment the core to obtain reasonable mineral attributes. Additionally, the model overcame the challenge of differentiating minerals with similar densities in μCT, which would not be possible with conventional segmentation methods. The approach is universal and can be extended to any multimodal and multi-instrument analysis for further refinement. We conclude that the combination of μCT and μXRF can provide a new opportunity for robust 3D mineral liberation analysis in both field and laboratory applications. Full article

(This article belongs to the Special Issue Application of Machine Learning in Rock Characterization)

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15 pages, 4178 KiB

Open AccessArticle

Permeability Prediction Using Machine Learning Methods for the CO₂ Injectivity of the Precipice Sandstone in Surat Basin, Australia

by Reza Rezaee and Jamiu Ekundayo

Energies 2022, 15(6), 2053; https://doi.org/10.3390/en15062053 - 11 Mar 2022

Cited by 16 | Viewed by 3436

Abstract

This paper presents the results of a research project which investigated permeability prediction for the Precipice Sandstone of the Surat Basin. Machine learning techniques were used for permeability estimation based on multiple wireline logs. This information improves the prediction of CO₂ injectivity in this formation. Well logs and core data were collected from 5 boreholes in the Surat Basin, where extensive core data and complete sets of conventional well logs exist for the Precipice Sandstone. Four different machine learning (ML) techniques, including Random Forest (RF), Artificial neural network (ANN), Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR), were independently trained with a wide range of hyper-parameters to ensure that not only is the best model selected, but also the right combination of model parameters is selected. Cross-validation for 20 different combinations of the seven available input logs was used for this study. Based on the performances in the validation and blind testing phases, the ANN with all seven logs used as input was found to give the best performance in predicting permeability for the Precipice Sandstone with the coefficient of determination (R²) of about 0.93 and 0.87 for the training and blind data sets respectively. Multi-regression analysis also appears to be a successful approach to calculate reservoir permeability for the Precipice Sandstone. Models with a complete set of well logs can generate reservoir permeability with R² of more than 90%. Full article

(This article belongs to the Special Issue Application of Machine Learning in Rock Characterization)

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16 pages, 47800 KiB

Open AccessArticle

Synthesizing Nuclear Magnetic Resonance (NMR) Outputs for Clastic Rocks Using Machine Learning Methods, Examples from North West Shelf and Perth Basin, Western Australia

by Reza Rezaee

Energies 2022, 15(2), 518; https://doi.org/10.3390/en15020518 - 12 Jan 2022

Cited by 7 | Viewed by 2889

Abstract

A nuclear magnetic resonance (NMR) logging tool can provide important rock and fluid properties that are necessary for a reliable reservoir evaluation. Pore size distribution based on T₂ relaxation time and resulting permeability are among those parameters that cannot be provided by conventional logging tools. For wells drilled before the 1990s and for many recent wells there is no NMR data available due to the tool availability and the logging cost, respectively. This study used a large database of combinable magnetic resonance (CMR) to assess the performance of several well-known machine learning (ML) methods to generate some of the NMR tool’s outputs for clastic rocks using typical well-logs as inputs. NMR tool’s outputs, such as clay bound water (CBW), irreducible pore fluid (known as bulk volume irreducible, BVI), producible fluid (known as the free fluid index, FFI), logarithmic mean of T₂ relaxation time (T_2LM), irreducible water saturation (S_wirr), and permeability from Coates and SDR models were generated in this study. The well logs were collected from 14 wells of Western Australia (WA) within 3 offshore basins. About 80% of the data points were used for training and validation purposes and 20% of the whole data was kept as a blind set with no involvement in the training process to check the validity of the ML methods. The comparison of results shows that the Adaptive Boosting, known as AdaBoost model, has given the most impressive performance to predict CBW, FFI, permeability, T_2LM, and S_Wirr for the blind set with R2 more than 0.9. The accuracy of the ML model for the blind dataset suggests that the approach can be used to generate NMR tool outputs with high accuracy. Full article

(This article belongs to the Special Issue Application of Machine Learning in Rock Characterization)

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19 pages, 5167 KiB

Open AccessArticle

Well-Logging Prediction Based on Hybrid Neural Network Model

by Lei Wu, Zhenzhen Dong, Weirong Li, Cheng Jing and Bochao Qu

Energies 2021, 14(24), 8583; https://doi.org/10.3390/en14248583 - 20 Dec 2021

Cited by 19 | Viewed by 5326

Abstract

Well-logging is an important formation characterization and resource evaluation method in oil and gas exploration and development. However, there has been a shortage of well-logging data because Well-logging can only be measured by expensive and time-consuming field tests. In this study, we aimed to find effective machine learning techniques for well-logging data prediction, considering the temporal and spatial characteristics of well-logging data. To achieve this goal, the convolutional neural network (CNN) and the long short-term memory (LSTM) neural networks were combined to extract the spatial and temporal features of well-logging data, and the particle swarm optimization (PSO) algorithm was used to determine hyperparameters of the optimal CNN-LSTM architecture to predict logging curves in this study. We applied the proposed CNN-LSTM-PSO model, along with support vector regression, gradient-boosting regression, CNN-PSO, and LSTM-PSO models, to forecast photoelectric effect (PE) logs from other logs of the target well, and from logs of adjacent wells. Among the applied algorithms, the proposed CNN-LSTM-PSO model generated the best prediction of PE logs because it fully considers the spatio-temporal information of other well-logging curves. The prediction accuracy of the PE log using logs of the adjacent wells was not as good as that using the other well-logging data of the target well itself, due to geological uncertainties between the target well and adjacent wells. The results also show that the prediction accuracy of the models can be significantly improved with the PSO algorithm. The proposed CNN-LSTM-PSO model was found to enable reliable and efficient Well-logging prediction for existing and new drilled wells; further, as the reservoir complexity increases, the proxy model should be able to reduce the optimization time dramatically. Full article

(This article belongs to the Special Issue Application of Machine Learning in Rock Characterization)

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