Porosity prediction through well logging data: A combined approach of convolutional neural network and transformer model (CNN-transformer)

Physics of Fluids, Journal Year: 2024, Volume and Issue: 36(2)

Published: Feb. 1, 2024

Porosity, as a key parameter to describe the properties of rock reservoirs, is essential for evaluating permeability and fluid migration performance underground rocks. In order overcome limitations traditional logging porosity interpretation methods in face geological complexity nonlinear relationships, this study introduces CNN (convolutional neural network)-transformer model, which aims improve accuracy generalization ability prediction. CNNs have excellent spatial feature capture capabilities. The convolution operation can effectively learn mapping relationship local features, so better correlation well log. Transformer models are able complex sequence relationships between different depths or time points. This enables model integrate information from times, prediction accuracy. We trained on log dataset ensure that it has good ability. addition, we comprehensively compare CNN-transformer with other machine learning verify its superiority Through analysis experimental results, shows task introduction will bring new perspective development technology provide more efficient accurate tool field geoscience.

Language: Английский

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Advanced AI approach for enhanced predictive modeling in reservoir characterization within complex geological environments

Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 10(4), P. 5043 - 5061

Published: June 14, 2024

Language: Английский

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A Comprehensive Study on Optimizing Reservoir Potential: Advanced Geophysical Log Analysis of Zamzama Gas Field, Southern Indus Basin, Pakistan

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2024, Volume and Issue: 135, P. 103640 - 103640

Published: May 20, 2024

Language: Английский

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Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves

Energies, Journal Year: 2024, Volume and Issue: 17(15), P. 3768 - 3768

Published: July 31, 2024

Porosity assessment is a vital component for reservoir evaluation in the oil and gas sector, with technological advancement, reliance on conventional methods has decreased. In this regard, research aims to reduce well logging, purposing successive machine learning (ML) techniques precise porosity measurement. So, examines prediction of curves Sui main upper limestone reservoir, utilizing ML approaches such as an artificial neural networks (ANN) fuzzy logic (FL). Thus, input dataset includes gamma ray (GR), neutron (NPHI), density (RHOB), sonic (DT) logs amongst five drilled wells located Qadirpur field. The ANN model was trained using backpropagation algorithm. For FL model, ten bins were utilized, Gaussian-shaped membership functions chosen ideal correspondence geophysical log dataset. closeness fit (C-fit) values ranged from 91% 98%, while exhibited variability 90% 95% throughout wells. addition, similar used evaluate multiple linear regression (MLR) comparative analysis. models achieved robust performance compared MLR, R2 0.955 (FL) 0.988 0.94 (MLR). outcomes indicate that exceed MLR predicting curve. Moreover, significant lowest root mean square error (RMSE) support potency these advanced approaches. This emphasizes authenticity not only enhance natural resource exploitation within region but also hold broader potential worldwide applications assessment.

Language: Английский

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Advanced Permeability Prediction Through Two-Dimensional Geological Feature Image Extraction with CNN Regression from Well Logs Data

Mathematical Geosciences, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 14, 2025

Language: Английский

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Multi-step modeling of well logging data combining unsupervised and deep learning algorithms for enhanced characterization of the Quaternary aquifer system in Debrecen area, Hungary

Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 10(3), P. 3693 - 3709

Published: March 22, 2024

Abstract In this research, a multi-step modeling approach is followed using unsupervised and deep learning algorithms to interpret the geophysical well-logging data for improved characterization of Quaternary aquifer system in Debrecen area, Hungary. The Most Frequent Value-Assisted Cluster Analysis (MFV-CA) used map lithological variations within system. Additionally, Csókás method discern both vertical horizontal fluctuations hydraulic conductivity. MFV-CA introduced cope with limitation conventional Euclidean distance-based k-means clustering known its low resistance outlying values, resulting deformed cluster formation. However, computational time demands are evident, making them costly time-consuming. As result, Deep Learning (DL) methods suggested provide fast groundwater aquifers. These include Multi-Layer Perceptron Neural Networks (MLPNN), Convolutional (CNN), Recurrent (RNN), Long Short-Term Memory (LSTM), which implemented classification regression. categorized inputs into three distinct lithologies trained initially by results MFV-CA. At same time, regression model offered continuous estimations conductivity model. demonstrated significant compatibility between outcomes derived from approaches DL algorithms. Accordingly, lithofacies across main hydrostratigraphical units mapped. This integration enhanced understanding system, offering promising development management.

Language: Английский

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A Gamma-ray spectroscopy approach to evaluate clay mineral composition and depositional environment: A case study from the lower Goru Formation, Southern Indus Basin, Pakistan

Journal of Applied Geophysics, Journal Year: 2024, Volume and Issue: 226, P. 105414 - 105414

Published: May 29, 2024

Language: Английский

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A Novel Hybrid Machine Learning Approach and Basin Modeling for Thermal Maturity Estimation of Source Rocks in Mandawa Basin, East Africa

Natural Resources Research, Journal Year: 2024, Volume and Issue: 33(5), P. 2089 - 2112

Published: June 26, 2024

Language: Английский

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High-resolution characterization of complex groundwater systems using wireline logs analyzed with machine learning classifiers and isometric mapping techniques

Modeling Earth Systems and Environment, Journal Year: 2025, Volume and Issue: 11(2)

Published: Jan. 20, 2025

Language: Английский

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Improved Reservoir Porosity Estimation Using an Enhanced Group Method of Data Handling with Differential Evolution Model and Explainable Artificial Intelligence

SPE Journal, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: Feb. 1, 2025

Summary Reservoir characterization is critical to the oil and gas industry, influencing field development, production optimization, hydraulic fracturing, reserves estimation decisions. Accurately estimating porosity crucial for reservoir characterization, well planning, optimization in industry. Traditional determination methods, such as porosimetry, geostatistical, core analysis, often involve complex geological geophysical models, which are expensive time-consuming. This study used integrated machine learning model of differential evolution (DE) with group method data handling (GMDH-DE) estimate using log from Mpyo field, Uganda. The GMDH-DE demonstrates superior performance compared conventional GMDH, support vector regression (SVR), random forest (RF), achieving a coefficient (R2) 0.9925 root mean square error (RMSE) 0.0017 during training, an R² 0.9845 RMSE 0.0121 testing, when validated R2 was 0.9825 0.00018. A key novelty this work integration Shapley additive explanations (SHAP), provides interpretable analysis model’s input features. SHAP reveals that bulk density (RHOB) neutron (NPHI) most parameters estimation, offering valuable insight into features importance. proposed represent novel independent approach accurate interpretability, significantly enhancing efficiency reliability hydrocarbon exploration development.

Language: Английский

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