Classification of reservoir quality using unsupervised machine learning and cluster analysis: Example from Kadanwari gas field, SE Pakistan

Ali, Nafees; Chen, Jian; Fu, Xiaodong; Hussain, Wakeel; Ali, Muhammad; Iqbal, Sayed Muhammad; Anees, Aqsa; Hussain, Mazahir; Rashid, Muhammad; Thanh, Hung Vo

doi:10.1016/j.geogeo.2022.100123

Cited by 22 publications

(5 citation statements)

References 42 publications

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“…The petrophysical analysis provides the key information on subsurface formations and resource evaluation that are essential for reservoir characterization. The evaluation of petrophysical parameters, such as shale/clay volume, effective porosity, water saturation, and facies, plays a significant role in the gas and oil sector. , Conventional petrophysical techniques are well-established that have been used for decades in the industry. However, these methods can be time-consuming and labor-intensive and require specialized expertise and equipment.…”

Section: Resultsmentioning

confidence: 99%

“…One of the most crucial considerations in the appraisal of a formation is the distribution of the shale, as the presence of shale affects the reservoir’s effective porosity and permeability. The γ-ray index is used to find the amount of clay inside the formations, and this information is then used to calculate the volume of the shale. − …”

Section: Materials and Methodologymentioning

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: Materials and Methodologymentioning

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

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“…Artificial intelligence (AI), an essential part of the engineering toolkit in recent decades, has been used to solve various environmental and engineering problems. ,− Machine learning (ML) is a subfield of AI that encompasses a variety of data processing techniques, including classification, regression, and clustering . Supervised and unsupervised techniques are the two broad divisions of ML. , Because of the ability of ML algorithms to recognize patterns and provide valuable predictions, the use of data-driven/ML algorithms has gained much attention in the energy, oil, and gas industry. ,,− Data-driven and ML techniques have been used when sufficient core data or, similar to most previous studies, a combination of core data and well-logging/seismic data are available to build predictive models. ,− The principal use of data-driven strategies and ML algorithms in petrophysics is rock typing and permeability predictions. ,− Most studies in the literature used different variations of support vector machine (SVM) and artificial neural network (ANN) to classify the reservoir into homogeneous clusters and then predict the permeability of each cluster. Moreover, most previous studies rely upon a combination of core data and well-logging/seismic data as inputs in their models.…”

Section: Introductionmentioning

confidence: 99%

“… 3 Supervised and unsupervised techniques are the two broad divisions of ML. 31 , 32 Because of the ability of ML algorithms to recognize patterns and provide valuable predictions, the use of data-driven/ML algorithms has gained much attention in the energy, oil, and gas industry. 1 , 14 , 33 − 36 Data-driven and ML techniques have been used when sufficient core data or, similar to most previous studies, a combination of core data and well-logging/seismic data are available to build predictive models.…”

Section: Introductionmentioning

confidence: 99%

Modeling Permeability Using Advanced White-Box Machine Learning Technique: Application to a Heterogeneous Carbonate Reservoir

Zhao,

Guo,

Mohammadian

et al. 2023

ACS Omega

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From exploration to production, the permeability of reservoir rocks is essential for various stages of all types of hydrocarbon field development. In the absence of costly reservoir rock samples, having a reliable correlation to predict rock permeability in the zone(s) of interest is crucial. To predict permeability conventionally, petrophysical rock typing is done. This method divides the reservoir into zones of similar petrophysical properties, and the permeability correlation for each zone is independently developed. The challenge of this approach is that the success depends upon the reservoir's complexity and heterogeneity and the methods and parameters used for rock typing. As a result, in the case of heterogeneous reservoirs, conventional rock typing methods and indices fail to predict the permeability accurately. The target area is a heterogeneous carbonate reservoir in southwestern Iran with a permeability range of 0.1−127.0 md. In this work, two approaches were used. First, based on permeability, porosity, the radius of pore throats at mercury saturation of 35% (r35), and connate water saturation (S wc ) as inputs of K-nearest neighbors, the reservoir was classified into two petrophysical zones, and then, permeability for each zone was estimated. Due to the heterogeneous nature of the formation, the predicted permeability results needed to be more accurate. In the second part, we applied novel machine learning algorithms, modified group modeling data handling (GMDH), and genetic programming (GP) to develop one universal permeability equation for the whole reservoir of interest as a function of porosity, the radius of pore throats at mercury saturation of 35% (r35), and connate water saturation (S wc ). The novelty of the current approach is that despite being universal, the models developed using GP and GMDH performed substantially better than zone-specific permeability, index-based empirical, or data-driven models used in the literature, such as FZI and Winland. The predicted permeability using GMDH and GP resulted in accurate prediction with R 2 of 0.99 and 0.95, respectively, in the heterogeneous reservoir of interest. Moreover, as this study aimed to develop an explainable model, different parameter importance analyses were also applied to the developed permeability models, and r35 was found to be the most impactful feature.

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“…Nafees 2023 used the self-organizing map (SOM) for the recognition of lithofacies and successfully extended this application to non-cored wells. It solves the problem that supervised learning algorithms cannot identify lithofacies from coreless data [28]. In the field of lithofacies identification, unsupervised machine learning algorithms such as model-based clustering, K-means clustering, ward hierarchical partitioning, and SOM have been extensively utilized [29].…”

Section: Introductionmentioning

confidence: 99%

Application and Comparison of Machine Learning Methods for Mud Shale Petrographic Identification

et al. 2023

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Machine learning is the main technical means for lithofacies logging identification. As the main target of shale oil spatial distribution prediction, mud shale petrography is subjected to the constraints of stratigraphic inhomogeneity and logging information redundancy. Therefore, choosing the most applicable machine learning method for different geological characteristics and data situations is one of the key aspects of high-precision lithofacies identification. However, only a few studies have been conducted on the applicability of machine learning methods for mud shale petrography. This paper aims to identify lithofacies using commonly used machine learning methods. The study employs five supervised learning algorithms, namely Random Forest Algorithm (RF), BP Neural Network Algorithm (BPANN), Gradient Boosting Decision Tree Method (GBDT), Nearest Neighbor Method (KNN), and Vector Machine Method (SVM), as well as four unsupervised learning algorithms, namely K-means, DBSCAN, SOM, and MRGC. The results are evaluated using the confusion matrix, which provides the accuracy of each algorithm. The GBDT algorithm has better accuracy in supervised learning, while the K-means and DBSCAN algorithms have higher accuracy in unsupervised learning. Based on the comparison of different algorithms, it can be concluded that shale lithofacies identification poses challenges due to limited sample data and high overlapping degree of type distribution areas. Therefore, selecting the appropriate algorithm is crucial. Although supervised machine learning algorithms are generally accurate, they are limited by the data volume of lithofacies samples. Future research should focus on how to make the most of limited samples for supervised learning and combine unsupervised learning algorithms to explore lithofacies types of non-coring wells.

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Classification of reservoir quality using unsupervised machine learning and cluster analysis: Example from Kadanwari gas field, SE Pakistan

Cited by 22 publications

References 42 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

Modeling Permeability Using Advanced White-Box Machine Learning Technique: Application to a Heterogeneous Carbonate Reservoir

Application and Comparison of Machine Learning Methods for Mud Shale Petrographic Identification

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