Application of local linear neuro-fuzzy model in estimating reservoir water saturation from well logs

Mollajan, Amir

doi:10.1007/s12517-014-1526-4

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…Well-log data such as sonic, density, neutron porosity, and resistivity logs are used to predict water saturation in reservoirs. Models based on neural networks on their own [9], or in combination with other techniques such as fuzzy logic [16], [27], ensemble structures [28], Mutual Information (MI) [20], least-squares support vector machine (LS-SVM) [29], and subtractive clustering [2]. Traditional machine learning models such as multilayer perceptron (MLP), Support Vector Machine, Decision Tree Forest, and Tree Boost methods were used, in another study, to train models for predicting water saturation in tight gas reservoirs [19].…”

Section: ) Water Saturation Mappingmentioning

confidence: 99%

SAMA: Spatially-Aware Model-Agnostic Machine Learning Framework for Geophysical Data

et al. 2023

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Geophysical data are a form of spatial data that suffer from certain limitations when applying conventional machine learning algorithms and evaluation techniques. A key limitation is the these models is the lack of ability to generalize when deployed to predict from new unseen data. We address the problem of inaccurate assessments of machine learning model performance that stems from violating independence assumptions during the feature selection and evaluation phases of the learning process. Our proposed spatially-aware and model-agnostic (SAMA) framework provides a suite of spatially-aware feature generation, feature selection, and model validation algorithms that account for spatial characteristics of geophysical data. The framework is model agnostic, as it tackles data-related challenges that are not affected by the specific machine learning algorithm used to fit the data. To demonstrate the effectiveness of the proposed approach, it is applied to the water saturation mapping problem using a novel geophysical dataset to train a prediction model. The proposed spatially-aware models reached an R 2 of 0.620, an RM SE of 0.220 for predicting water saturation for the Whole Region of the reservoir model box and an R 2 of 0.161, an RM SE of 0.263 for the Interwell Region. Experiments with an additional 5 unseen datasets show that the model maintains stable performance across these different datasets, demonstrating the ability of the SAMA framework to produce robust models that are able to generalize to new datasets.

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Section: ) Water Saturation Mappingmentioning

confidence: 99%

SAMA: Spatially-Aware Model-Agnostic Machine Learning Framework for Geophysical Data

et al. 2023

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“…The authors presented clustering algorithms because they are unsupervised approaches that do not need real data for training the machine . By using well logs, the local linear neuro-fuzzy (LLNF) model was employed to estimate the water saturation in a carbonate reservoir . The study showed an application of ensemble tree-based algorithms in estimating the fluid saturation in oil sands .…”

Section: Introductionmentioning

confidence: 99%

New Correlation for Calculating Water Saturation Based on Permeability, Porosity, and Resistivity Index in Carbonate Reservoirs

et al. 2022

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Water saturation assessment is recognized as one of the most critical aspects of formation evaluation, reserve estimation, and prediction of the production performance of any hydrocarbon reservoir. Water saturation measurement in a core laboratory is a time-consuming and expensive task. Many scientists have attempted to estimate water saturation accurately using well-logging data, which provides a continuous record without information loss. As a result, numerous models have been developed to relate reservoir characteristics with water saturation. By expanding the use and advancement of soft computing approaches in engineering challenges, petroleum engineers applied them to estimate the petrophysical parameters of the reservoir. In this paper, two techniques are developed to estimate the water saturation in terms of porosity, permeability, and formation resistivity index through the use of 383 data sets obtained from carbonate core samples. These techniques are the nonlinear multiple regression (NLMR) technique and the artificial neural network (ANN) technique. The proposed ANN model achieved outstanding performance and better accuracy for calculating the water saturation than the empirical correlation using NLMR and Archie equation with a high coefficient of determination (R 2) of 0.99, a low average relative error of 1.92, a low average absolute relative error of 13.62, and a low root mean square error of 0.066. To the best of our knowledge, the current research establishes a novel foundation using the ANN model in the estimation of water saturation.

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“…The authors chose unsupervised methods because they do not require real labeled data. Study [24] presented an application of the local linear neuro-fuzzy (LLNF) model in estimating reservoir water saturation from well logs. This was followed by [25] aiming to evaluate fluid saturation in oil sands by means of ensemble tree-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Water Saturation from Well Log Data by Machine Learning Algorithms: Boosting and Super Learner

Hadavimoghaddam

Ostadhassan

Sadri

et al. 2021

JMSE

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Intelligent predictive methods have the power to reliably estimate water saturation (Sw) compared to conventional experimental methods commonly performed by petrphysicists. However, due to nonlinearity and uncertainty in the data set, the prediction might not be accurate. There exist new machine learning (ML) algorithms such as gradient boosting techniques that have shown significant success in other disciplines yet have not been examined for Sw prediction or other reservoir or rock properties in the petroleum industry. To bridge the literature gap, in this study, for the first time, a total of five ML code programs that belong to the family of Super Learner along with boosting algorithms: XGBoost, LightGBM, CatBoost, AdaBoost, are developed to predict water saturation without relying on the resistivity log data. This is important since conventional methods of water saturation prediction that rely on resistivity log can become problematic in particular formations such as shale or tight carbonates. Thus, to do so, two datasets were constructed by collecting several types of well logs (Gamma, density, neutron, sonic, PEF, and without PEF) to evaluate the robustness and accuracy of the models by comparing the results with laboratory-measured data. It was found that Super Learner and XGBoost produced the highest accurate output (R2: 0.999 and 0.993, respectively), and with considerable distance, Catboost and LightGBM were ranked third and fourth, respectively. Ultimately, both XGBoost and Super Learner produced negligible errors but the latest is considered as the best amongst all.

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Application of local linear neuro-fuzzy model in estimating reservoir water saturation from well logs

Cited by 9 publications

References 26 publications

SAMA: Spatially-Aware Model-Agnostic Machine Learning Framework for Geophysical Data

SAMA: Spatially-Aware Model-Agnostic Machine Learning Framework for Geophysical Data

New Correlation for Calculating Water Saturation Based on Permeability, Porosity, and Resistivity Index in Carbonate Reservoirs

Prediction of Water Saturation from Well Log Data by Machine Learning Algorithms: Boosting and Super Learner

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