Lithofacies prediction in non-cored wells from the Sif Fatima oil field (Berkine basin, southern Algeria): A comparative study of multilayer perceptron neural network and cluster analysis-based approaches

Ameur-Zaimeche, Ouafi; Zeddouri, Aziez; Heddam, Salim; Kechiched, Rabah

doi:10.1016/j.jafrearsci.2020.103826

Cited by 40 publications

(6 citation statements)

References 29 publications

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“…In particular, the CNN was the firstly applied to a two-dimensional matrix of production history. In the case of SVM, it is one of conventional machine learning algorithms, which are not based on the concept of neural network, and has shown reliable performance in facies classification [38][39][40]. In this study, it has been verified against the TI classification problem.…”

Section: Introductionmentioning

confidence: 85%

Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data

Kang

Lee

2020

Geofluids

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Training image (TI) has a great influence on reservoir modeling as a spatial correlation in the multipoint geostatistics. Unlike the variogram of the two-point geostatistics that is mathematically defined, there is a high degree of geological uncertainty to determine a proper TI. The goal of this study is to develop a classification model for determining the proper geological scenario among plausible TIs by using machine learning methods: (a) support vector machine (SVM), (b) artificial neural network (ANN), and (c) convolutional neural network (CNN). After simulated production data are used to train the classification model, the most possible TI can be selected when the observed production responses are put into the trained model. This study, as far as we know, is the first application of CNN in which production history data are composed as a matrix form for use as an input image. The training data are set to cover various production trends to make the machine learning models more reliable. Therefore, a total of 800 channelized reservoirs were generated from four TIs, which have different channel directions to consider geological uncertainty. We divided them into training, validation, and test sets of 576, 144, and 80, respectively. The input layer comprised 800 production data, i.e., oil production rates and water cuts for eight production wells over 50 time steps, and the output layer consisted of a probability vector for each TI. The SVM and CNN models reasonably reduced the uncertainty in modeling the facies distribution based on the reliable probability for each TI. Even though the ANN and CNN had roughly the same number of parameters, the CNN outperformed the ANN in terms of both validation and test sets. The CNN successfully classified the reference model’s TI with about 95% probability. This is because the CNN can grasp the overall trend of production history. The probabilities of TI from the SVM and CNN were applied to regenerate more reliable reservoir models using the concept of TI rejection and reduced the uncertainty in the geological scenario successfully.

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Section: Introductionmentioning

confidence: 85%

Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data

Kang

Lee

2020

Geofluids

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“…9 Al-Anazi and Gates 13 used SVM algorithms to classify sand and mud reservoirs. 10 Gao and Jiao 14 proposed a method for identifying rock types by combining three-dimensional vibration signal mixeddomain features with an SVM. This method transforms complex nonlinear problems into simple linear problems.…”

Section: Introductionmentioning

confidence: 99%

“…Ameur‐Zaimeche et al 10 discussed the application of a multilayer perceptron neural network (MLPNN) method to reconstruct a noncore lithofacies division algorithm. Feng et al proposed a Bayesian neural network (B‐ANN) lithofacies identification algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…A new lithology identification method based on an SVM (LDM KL‐SVM) was proposed to conduct multicore deep learning 9 . Al‐Anazi and Gates 13 used SVM algorithms to classify sand and mud reservoirs 10 . Gao and Jiao 14 proposed a method for identifying rock types by combining three‐dimensional vibration signal mixed‐domain features with an SVM.…”

Section: Introductionmentioning

confidence: 99%

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Lithofacies identification of shale formation based on mineral content regression using LightGBM algorithm: A case study in the Luzhou block, South Sichuan Basin, China

Liu,

Zhu,

Zhai

et al. 2023

Energy Science & Engineering

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Lithofacies form the basis for evaluating shale gas fields and play an important role in gas reservoir enrichment. The accurate identification of shale lithofacies is key for exploration and development. Based on well‐logged data, the accuracy of mineral content prediction using machine‐learning regression models is not ideal. Therefore, feature derivation was introduced to enhance the correlation between minerals and lithofacies and improve the data expression ability. Four machine‐learning models for mineral regression were established based on feature‐derived data sets: LightGBM, XGBoost, artificial neural network, and support vector machine. By calculating the evaluation metrics of each model, we found that LightGBM had the best prediction performance. To compare and confirm the accuracy of the model in identifying lithofacies, this study established a new method, MT‐LightGBM, which combines the LightGBM mineral content regression model with mineral ternary diagrams to identify lithofacies. By using the MT‐LightGBM model and LightGBM classification models to identify the target lithofacies, it was found that the accuracy of lithofacies identification of MT‐LightGBM reached 94%. This accuracy is high and is of great significance for understanding and evaluating underground shale reservoirs.

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“…Tang et al (2004) [14] used statistical methods for classifying electric log facies from awest African clastic reservoir. Avseth et al (2002) [18] used different multivariate statistical methods and a neural network for seismic lithofacies classification from well logs. Dubois et al (2007) [19] tried to classify rock facies using various techniques in the Panoma gas field in Southwest Kansas.…”

Section: Introductionmentioning

confidence: 99%

Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam

Man¹,

Hien²,

Thong

et al. 2021

Energies

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The test study area is the Miocene reservoir of Nam Con Son Basin, offshore Vietnam. In the study we used unsupervised learning to automatically cluster hydraulic flow units (HU) based on flow zone indicators (FZI) in a core plug dataset. Then we applied supervised learning to predict HU by combining core and well log data. We tested several machine learning algorithms. In the first phase, we derived hydraulic flow unit clustering of porosity and permeability of core data using unsupervised machine learning methods such as Ward’s, K mean, Self-Organize Map (SOM) and Fuzzy C mean (FCM). Then we applied supervised machine learning methods including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Boosted Tree (BT) and Random Forest (RF). We combined both core and log data to predict HU logs for the full well section of the wells without core data. We used four wells with six logs (GR, DT, NPHI, LLD, LSS and RHOB) and 578 cores from the Miocene reservoir to train, validate and test the data. Our goal was to show that the correct combination of cores and well logs data would provide reservoir engineers with a tool for HU classification and estimation of permeability in a continuous geological profile. Our research showed that machine learning effectively boosts the prediction of permeability, reduces uncertainty in reservoir modeling, and improves project economics.

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Lithofacies prediction in non-cored wells from the Sif Fatima oil field (Berkine basin, southern Algeria): A comparative study of multilayer perceptron neural network and cluster analysis-based approaches

Cited by 40 publications

References 29 publications

Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data

Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data

Lithofacies identification of shale formation based on mineral content regression using LightGBM algorithm: A case study in the Luzhou block, South Sichuan Basin, China

Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam

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