The Impact of Formation Anisotropy and Stresses on Fractural Geometry—A Case Study in Jafurah’s Tuwaiq Mountain Formation (TMF), Saudi Arabia

Shawaf, Ali; Rasouli, Vamegh; Dehdouh, Abdesselem

doi:10.3390/pr11051545

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…Tight reservoirs hold significant oil and gas reserves; however, their complex rock and fluid properties present production challenges [1]. Despite the application of multistage hydraulic fracturing, less than 20% of the original oil in place can be technically and economically producible [2]. Reservoir simulation results tend to overestimate production from unconventional reservoirs due to uncertainties in characterizing rock and fluid properties variation.…”

Section: Introductionmentioning

confidence: 99%

Water Saturation Prediction in the Middle Bakken Formation Using Machine Learning

Mellal,

Latrach,

Rasouli

et al. 2023

Eng

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Tight reservoirs around the world contain a significant volume of hydrocarbons; however, the heterogeneity of these reservoirs limits the recovery of the original oil in place to less than 20%. Accurate characterization is therefore needed to understand variations in reservoir properties and their effects on production. Water saturation (Sw) has always been challenging to estimate in ultra-tight reservoirs such as the Bakken Formation due to the inaccuracy of resistivity-based methods. While machine learning (ML) has proven to be a powerful tool for predicting rock properties in many tight formations, few studies have been conducted in reservoirs of similar complexity to the Bakken Formation, which is an ultra-tight, multimineral, low-resistivity reservoir. This study presents a workflow for Sw prediction using well logs, core data, and ML algorithms. Logs and core data were gathered from 29 wells drilled in the Bakken Formation. Due to the inaccuracy and lack of robustness of the tried and tested regression models (e.g., linear regression, random forest regression) in predicting Sw as a continuous variable, the problem was reformulated as a classification task. Instead of exact values, the Sw predictions were made in intervals of 10% increments representing 10 classes from 0% to 100%. Gradient boosting and random forest classifiers scored the best classification accuracy, and these two models were used to construct a voting classifier that achieved the best accuracy of 85.53%. The ML model achieved much better accuracy than conventional resistivity-based methods. By conducting this study, we aim to develop a new workflow to improve the prediction of Sw in reservoirs where conventional methods have poor performance.

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Section: Introductionmentioning

confidence: 99%

Water Saturation Prediction in the Middle Bakken Formation Using Machine Learning

Mellal,

Latrach,

Rasouli

et al. 2023

Eng

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“…Shale gas reservoirs have a very low permeability and no natural production capacity. To develop shale gas, oil and gas reservoirs must be stimulated [1]. Hydraulic fracturing is a common means of reservoir stimulation [2,3].…”

Section: Introductionmentioning

confidence: 99%

Fracture Toughness Testing of Brittle Laminated Geomaterials Using Hollow Double-Wing Slotted Specimens

Yao,

Fan,

Zhou

et al. 2023

Materials

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The fracture toughness of shale is a key parameter guiding hydraulic fracturing design and optimization. The hollow double-wing slotted (HDWS) specimen is a typical specimen configuration for measuring the mode I fracture toughness of rock. The calibration of the shape factor (f) is the basis for accurately obtaining the fracture toughness of rocks. In this study, the influences of crack length, hole size, and the anisotropy of elastic parameters on f for specimens with three typical bedding orientations—arrester (A), divider (D), and short-transverse (ST) orientations—are systematically investigated using finite element software. The numerical simulation results support the following findings. The mode I f increases monotonically with an increase in hole size. The influence of crack length on f varies depending on hole sizes. Under different bedding orientations, significant anisotropy in f was observed. In addition, the degree of anisotropy in Young’s modulus has a major impact on f, which is related to the bedding orientation of the specimen. The apparent shear modulus ratio has relatively little influence on f. As the hole size and crack length increase, the influence of the anisotropy of elastic parameters on f increases. Based on numerical calculations, hydraulic fracturing experiments were conducted on HDWS specimens of Longmaxi shale with three bedding orientations, and the results showed that the peak pressure and fracture toughness of the samples in the ST direction were the lowest, while those in the A direction were the highest.

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“…The anisotropic medium is another factor that raises the reservoir characterization complexity . According to Shawaf et al, (2023), the anisotropic rock mechanical behavior has a direct impact on the stimulation parameters, well trajectory, and well stability. A deep understanding of the elastic properties is essential for an accurate and representative reservoir characterization which directly contributes to the success of field development projects and future decisions.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Shear Velocity Log Prediction in Ahnet Field, Algeria, Through Well Logs and Machine Learning Techniques

Goucem

2023

Preprint

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Shear velocity logs are crucial in the oil and gas industry for assessing subsurface mechanical properties, including rock stiffness, shear strength, and seismic wave propagation, essential for optimizing hydrocarbon exploration and production strategies. However, obtaining shear velocity logs conventionally is expensive and time-consuming, especially when drilling additional wells solely for this purpose. With the recent boom in machine learning algorithms adoption across various scientific domains, it proved to be an extremely valuable tool for numerous applications in the oil and gas industry. It makes use of the readily available large datasets collected over decades and leverages this data to train powerful, data-driven models, reducing the reliance on empirical relationships that usually have poor generalization. This study follows this approach and presents the use and comparison of machine learning algorithms for predicting shear velocity logs from conventional and readily available logs in the Ahnet field, Algeria. Ultimately, this study aims to enhance reservoir assessment and optimize hydrocarbon recovery processes, potentially reducing exploration costs and improving oil and gas production decision-making in the region.

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The Impact of Formation Anisotropy and Stresses on Fractural Geometry—A Case Study in Jafurah’s Tuwaiq Mountain Formation (TMF), Saudi Arabia

Cited by 6 publications

References 32 publications

Water Saturation Prediction in the Middle Bakken Formation Using Machine Learning

Water Saturation Prediction in the Middle Bakken Formation Using Machine Learning

Fracture Toughness Testing of Brittle Laminated Geomaterials Using Hollow Double-Wing Slotted Specimens

Enhancing Shear Velocity Log Prediction in Ahnet Field, Algeria, Through Well Logs and Machine Learning Techniques

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