Integrating machine learning and data analytics for geostatistical characterization of clastic reservoirs

Al‐Mudhafar, Watheq J.

doi:10.1016/j.petrol.2020.107837

Cited by 43 publications

(14 citation statements)

References 22 publications

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“…Al-Mudhafart integrated ML and data analytics for clastic reservoir facies and discovered that LogitBoost is the most accurate algorithm, with 100% accuracy in total correct facies prediction. However, the total correct percentages for Multinom and XGBoost were 80.24 and 70.83%, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Farouk et al deployed core data and two-hybrid ML algorithms, including PSO-trained neural networks and least-squares support vector machines (SVM), to evaluate the permeability of the reservoirs. Likewise, Al-Mudhafar employed a combination of machine learning and data analytics to enhance the geostatistical characterization of clastic reservoirs in the Luhais oil field. Male et al performed a comparative analysis between physics and machine-learning-based approaches for predicting permeability in cemented sandstones.…”

Section: Introductionmentioning

confidence: 99%

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Harnessing Advanced Machine-Learning Algorithms for Optimized Data Conditioning and Petrophysical Analysis of Heterogeneous, Thin Reservoirs

Manzoor

Ehsan

Hussain³

et al. 2023

Energy Fuels

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Petrophysical analysis is an industry-standard practice for reservoir evaluation as it provides critical inputs for characterizing subsurface formations and estimating resource potential. Khadro/Ranikot Formation sands are proliferous producers in the Central Indus Basin, Pakistan. The demarcate potential in intercalated sand shale layers that are thin and heterogeneous makes it a challenging reservoir. Conventional petrophysical interpretation is laborious and does not produce upto-mark results due to reservoir complexity, data limitations, and associated uncertainties. Hence, an emerging and delicate machinelearning (ML) approach has been comprehensively applied to analyze the potential and robustly interpret well log data while addressing the associated challenges. This case study entails a thorough evaluation of well log quality, assessing several algorithms such as least-squares support vector machines (one-class SVM), Random Forest Regressor (RFR), Extra Tree Regressor (ETR), Gradient Boosting Regressor (GBR), Decision Tree Classifier (DTC), etc. to compare their efficacy and reliability. One-class SVM helps to reduce outliers with great certainty, while the missing logs sonic (DT) and density (RHOB) are precisely predicted via GBR and ETR with 0.66 and 0.88 R 2 , respectively. Hence, providing reliable and optimized quality logs suitable for ML-based petrophysics. ML worked on these augmented logs by dividing the data into 60% training and 40% testing. The ETR outperformed the rest of the models with a correlation of 0.99 and 0.91 among conventional and ML results. Likewise, RFR performed exceptionally well for water saturation modeling, expressing the highest 0.93 correlation. Finally, DTC modeled reservoir facies with the best 91% accuracy and 0.935 F1 measures at the blind well. Excellent calibration of >85% is met with the estimates obtained by the predictive model compared to conventional methods. This comprehensive approach offers cost-effective and robust workarounds for modern formation evaluation with minimal uncertainty and resource-efficient multiwell interpretation within complex reservoirs and sets the stage for further research in the machine-learning ecosystem.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Harnessing Advanced Machine-Learning Algorithms for Optimized Data Conditioning and Petrophysical Analysis of Heterogeneous, Thin Reservoirs

Manzoor

Ehsan

Hussain³

et al. 2023

Energy Fuels

View full text Add to dashboard Cite

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“…In recent years, many scholars have applied it to the field of seismic exploration (Lin et al, 2018;He et al, 2020). For example, reservoir fracture parameter prediction (Xue et al, 2014;Yasin et al, 2022), lithology identification (Al-Mudhafar, 2020;Alzubaidi et al, 2021;Saporetti et al, 2021) and seismic inversion (Li et al, 2019;Cao et al, 2021), etc.…”

Section: Figurementioning

confidence: 99%

Simultaneous prediction of multiple physical parameters using gated recurrent neural network: Porosity, water saturation, shale content

et al. 2022

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Reservoir parameter prediction is of significant value to oil and gas exploration and development. Artificial intelligence models are developing rapidly in reservoir parameter prediction. Unfortunately, current research has focused on multi-input single-output prediction models. Meaning, these models use a large amount of logging or seismic data to predict the petrophysical properties of a single reservoir. Another prominent problem is that most mechanistic learning studies have focused on using logging data (e.g., gamma ray and resistivity) to make predictions of reservoir parameters. Although these studies have yielded promising accuracy, a great shortcoming is the inability to obtain such data in logs by seismic inversion. The value of our research work is to achieve a complete description of the reservoir using the elastic parameters from the seismic inversion. We developed a deep learning method based on gated recurrent neural network (GRNN) suitable for simultaneous prediction of porosity, saturation and shale content in the reservoir. GRNN is based on Gated Recurrent Unit (GRU), which can automatically update and reset the hidden state. The input parameters to the model are compressive wave velocity, shear wave velocity and density. The model is trained to fit nonlinear relationships between input parameters and multiple physical parameters. We employed two wells: one for testing and the other for training. 20% of the data in the training wells were used as the validation set. In preprocessing, we performed z-score whitening on the input data. During the training phase, the model hyperparameters were optimized based on the mean absolute error (MAE) box plots of the validation set. Experiments on the test data show that the model has superior robustness and accuracy compared to the conventional recurrent neural network (RNN). In the GRNN prediction results of the test set, the MAE is 0.4889 and the mean squared error (MSE) is 0.5283. Due to the difference in input parameters, our prediction is weaker than the research method using logging data. However, our proposed method has higher practical value in exploration work.

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“…Generally, some linear regression analysis approach is used for relating the permeability to existing petrophysical parameters (Dahraj and Bhutto 2014;Handhal 2016). Multivariate statistical tools have been applied for permeability estimation in Al-Mudhafar (2020).…”

Section: Introductionmentioning

confidence: 99%

Permeability extraction from multiple well logs using particle swarm optimization based factor analysis

2022

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In this paper, we present an innovative factor analysis algorithm for hydrocarbon exploration to estimate the intrinsic permeability of reservoir rocks from well logs. Unlike conventional evaluation methods that employ a single or a limited number of data types, we process simultaneously all available data to derive the first statistical factor and relate it to permeability by regression analysis. For solving the problem of factor analysis, we introduce an improved particle swarm optimization method, which searches for the global minimum of the distance between the observed and calculated data and gives a quick estimation for the factor scores. The learning factors of the intelligent computational technique such as the cognitive and social constants are specified as hyperparameters and calculated by using simulated annealing algorithm as heuristic hyperparameter estimator. Instead of the arbitrary fixation of these hyperparameters, we refine them in an iterative process to give reliable estimation both for the statistical factors and formation permeability. The estimated learning parameters are consistent with literature recommendations. We demonstrate the feasibility of the proposed well-log analysis method by a Hungarian oilfield study involving open-hole wireline logs and core data. We determine the spatial distribution of permeability both along a borehole and between more wells using the factor analysis approach, which serves as efficient and reliable multivariate statistical tool for advanced formation evaluation and reservoir modeling.

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Integrating machine learning and data analytics for geostatistical characterization of clastic reservoirs

Cited by 43 publications

References 22 publications

Harnessing Advanced Machine-Learning Algorithms for Optimized Data Conditioning and Petrophysical Analysis of Heterogeneous, Thin Reservoirs

Harnessing Advanced Machine-Learning Algorithms for Optimized Data Conditioning and Petrophysical Analysis of Heterogeneous, Thin Reservoirs

Simultaneous prediction of multiple physical parameters using gated recurrent neural network: Porosity, water saturation, shale content

Permeability extraction from multiple well logs using particle swarm optimization based factor analysis

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