Data analytics and Bayesian Optimised Extreme Gradient Boosting approach to estimate cut-offs from wireline logs for net reservoir and pay classification

Otchere, Daniel Asante; Ganat, Tarek Al-Arbi Omar; Nta, Vanessa; Brantson, Eric Thompson; Sharma, Tushar

doi:10.1016/j.asoc.2022.108680

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…K‐means is widely used in the petroleum field owing to its low computational complexity and fast processing speed (Huang et al, 2014; Liu et al, 2022; Otchere et al, 2022; Zahra et al, 2015). Figure 3 shows a flow chart of the K‐means algorithm used in this study.…”

Section: Methodsmentioning

confidence: 99%

Quantitative evaluation of unconsolidated sandstone heavy oil reservoirs based on machine learning

et al. 2022

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Classification and evaluation of reservoirs conforming to geological characteristics are the proposed premises of the high‐quality development of unconsolidated sandstone heavy oil reservoirs. The P oilfield in Bohai Bay is one of the largest offshore oilfields in China, but the reservoirs are highly heterogeneous, have complex pore structures, and change fluid properties, which make the classification of reservoirs very difficult. At present, the most commonly used reservoir classification methods are based on the results of logging interpretation, which cannot eliminate the constraints of linear relationships such as empirical formulas. Machine learning has significant advantages for identifying reservoir classification problems that are complex and nonlinear with geological characteristics. First, this study fully considered the pore structure and fluid properties and selected four representative evaluation parameters. An improved K‐means method was used to establish a reservoir classification evaluation system. Characteristics, such as the sedimentary microfacies, pore structure and oilfield production performance, of different types of reservoirs were then analysed. Finally, six logging curves were selected as input results, reservoir classification was used as an output result, and XGBoost was used to carry out reservoir classification evaluation for the entire area. The accuracy of XGBoost was 85.35%. Furthermore, XGBoost was compared with three other machine learning methods to confirm its reliability. The results showed that the reservoirs in the study area can be divided into three types. Sandstone thickness and pore structure were the most important parameters that affected reservoir quality. Among various machine learning algorithms, XGBoost can accurately and quickly achieve a quantitative evaluation of reservoir quality.

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Section: Methodsmentioning

confidence: 99%

Quantitative evaluation of unconsolidated sandstone heavy oil reservoirs based on machine learning

et al. 2022

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“…Additionally, GMM provides a soft-clustering approach, assigning probabilities of membership to each point for all clusters, rather than forcing a hard assignment. This results in a more nuanced understanding of the data's structure, particularly useful when the relationship between variables is complex and not easily separable into distinct groupings 47 . Hence, incorporating both K-means and Gaussian Mixture Model (GMM) methods in a single study leverages the strengths of both clustering techniques.…”

Section: Machine Learning Descriptionmentioning

confidence: 99%

Utilizing machine learning for flow zone indicators prediction and hydraulic flow unit classification

Astsauri,

Habiburrahman,

Ibrahim

et al. 2024

Sci Rep

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Reservoir characterization, essential for understanding subsurface heterogeneity, often faces challenges due to scale-dependent variations. This study addresses this issue by utilizing hydraulic flow unit (HFU) zonation to group rocks with similar petrophysical and flow characteristics. Flow Zone Indicator (FZI), a crucial measure derived from pore throat size, permeability, and porosity, serves as a key parameter, but its determination is time-consuming and expensive. The objective is to employ supervised and unsupervised machine learning to predict FZI and classify the reservoir into distinct HFUs. Unsupervised learning using K-means clustering and supervised algorithms including Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were employed. FZI values from RCAL data formed the basis for model training and testing, then the developed models were used to predict FZI in unsampled locations. A methodical approach involves 3 k-fold cross-validation and hyper-parameter tuning, utilizing the random search cross-validation technique over 50 iterations was applied to optimize each model. The four applied algorithms indicate high performance with coefficients determination (R2) of 0.89 and 0.91 in training and testing datasets, respectively. RF showed the heist performance with training and testing R2 values of 0.957 and 0.908, respectively. Elbow analysis guided the successful clustering of 212 data points into 10 HFUs using k-means clustering and Gaussian mixture techniques. The high-quality reservoir zone was successfully unlocked using the unsupervised technique. It has been discovered that the areas between 2370–2380 feet and 2463–2466 feet are predicted to be high-quality reservoir potential areas, with average FZI values of 500 and 800, consecutively. The application of machine learning in reservoir characterization is deemed highly valuable, offering rapid, cost-effective, and precise results, revolutionizing decision-making in field development compared to conventional methods.

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“…In the research from Otchere et al’s study [ 106 , 108 ], which focuses on analysis in the reservoir domain, specifically using the non-temporal Equinor Volve Field datasets, two models employed Bayesian Optimization with XGBoost (BayesOpt-XGBoost) and XGBoost. The dataset comprised 2853 samples, and the classification task involved DT, GR, NPHI, RT, and RHOB as features, aiming to predict Vshale, porosity, and water saturation (Sw).…”

Section: Predicted Analytics Models For Oandgmentioning

confidence: 99%

A Review of Predictive Analytics Models in the Oil and Gas Industries

R Azmi,

Yusoff,

Mohd Sallehud-din

2024

Sensors

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Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry’s predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model’s categories, the data’s temporality, field, and name, the dataset’s type, predictive analytics (classification, clustering, or prediction), the models’ input and output parameters, the performance metrics, the optimal model, and the model’s benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries.

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Data analytics and Bayesian Optimised Extreme Gradient Boosting approach to estimate cut-offs from wireline logs for net reservoir and pay classification

Cited by 11 publications

References 12 publications

Quantitative evaluation of unconsolidated sandstone heavy oil reservoirs based on machine learning

Quantitative evaluation of unconsolidated sandstone heavy oil reservoirs based on machine learning

Utilizing machine learning for flow zone indicators prediction and hydraulic flow unit classification

A Review of Predictive Analytics Models in the Oil and Gas Industries

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