A Method for Judging the Effectiveness of Complex Tight Gas Reservoirs Based on Geophysical Logging Data and Using the L Block of the Ordos Basin as a Case Study

Zhao, Qing; Guo, Jianhong; Zhang, Zhansong

doi:10.3390/pr11072195

Cited by 2 publications

(4 citation statements)

References 29 publications

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“…where p = (p 1 , p 2 , ..., p n ) q = (q 1 , q 2 , ..., q n ) Euclidean distance measures the distance solely based on the magnitudes of the differences between the corresponding vector components, and discounts the vectors' similarity in their hyper-planes. Cosine similarity, θ c (p, q), on the other hand, uses the hyperplane on which two vectors lie to define the similarity between two vectors as shown in Equation (3). Here, the similarity can be used as a proxy for the distance between two vectors: the higher the similarity, the lower the distance, and vice versa.…”

Section: Methodsmentioning

confidence: 99%

“…Lithofacies identification is an essential tool in geologic investigation, where a rock type or class is assigned to a specific rock sample based on measured properties [1]. The accuracy of this identification process directly affects reservoir evaluation and development plan formulation [2,3]. The traditional approach to lithofacies identification uses core-and thin-section analysis from cored-well matched with well-logs [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Lithofacies Identification from Wire-Line Logs Using an Unsupervised Data Clustering Algorithm

Hasan,

Shahidi

et al. 2023

Energies

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Stratigraphic identification from wire-line logs and core samples is a common method for lithology classification. This traditional approach is considered superior, despite its significant financial cost. Artificial neural networks and machine learning offer alternative, cost-effective means for automated data interpretation, allowing geoscientists to extract insights from data. At the same time, supervised and semi-supervised learning techniques are commonly employed, requiring a sufficient amount of labeled data to be generated through manual interpretation. Typically, there are abundant unlabeled geophysical data while labeled data are scarcer. Supervised and semi-supervised techniques partially address the cost issue. An underutilized class of machine-learning-based methods, unsupervised data clustering, can perform consonant classification by grouping similar data without requiring known results, presenting an even more cost-effective solution. In this study, we examine a state-of-the-art unsupervised data clustering algorithm called piecemeal clustering to identify lithofacies from wire-line logs, effectively addressing these challenges. The piecemeal clustering algorithm groups similar wire-log signatures into clusters, determines the number of clusters present in the data, and assigns each signature to one of the clusters, each of which represents a lithofacies. To evaluate the performance, we tested the algorithm on publicly released data from ten wells drilled in the Hugoton and Panoma fields of southwest Kansas and northwest Oklahoma, respectively. The data consist of two major groups: marine and non-marine facies. The study herein is centered around addressing two fundamental research questions regarding the accuracy and practicality of the piecemeal clustering algorithm. The algorithm successfully identified nine distinct clusters in our dataset, aligning with the cluster count observed in previously published works employing the same data. Regarding mapping accuracy, the results were notable, with success rates of 81.90% and 45.20% with and without considering adjacent facies, respectively. Further detailed analysis of the results was conducted for individual types of facies and independently for each well. These findings suggest the algorithm’s precision in characterizing the geological formations. To assess its performance, a comprehensive comparative analysis was conducted, encompassing other data clustering algorithms, as well as supervised and semi-supervised machine learning techniques. Notably, the piecemeal clustering algorithm outperformed alternative data clustering methods. Furthermore, despite its unsupervised nature, the algorithm demonstrated competitiveness by yielding results comparable to, or even surpassing, those obtained through supervised and semi-supervised techniques.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lithofacies Identification from Wire-Line Logs Using an Unsupervised Data Clustering Algorithm

Hasan,

Shahidi

et al. 2023

Energies

View full text Add to dashboard Cite

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“…2. The discussion part of Reference [6] reveals the influence of the invasion phenomenon on resistivity measurement, and the fractured reservoir is more obviously affected by the invasion. In order to ensure the reliability of resistivity logging response value, the interval between drilling time and logging time should be shortened as much as possible.…”

Section: Recommendationsmentioning

confidence: 99%

“…The development of fractures stands out as a key characteristic influencing reservoir storage capacity [5]. In China's major oil and gas fields, low-permeability fractured reservoirs are prevalent and are poised to serve as a crucial resource base for bolstering reserves and production in the country's oil industry moving forward [6]. The increase in rock density in low-permeability fractured reservoirs leads to heightened rock strength and brittleness [7].…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation Study on the Influence of Cracks in a Full-Size Core on the Resistivity Measurement Response

Zheng,

Zhang,

Guo

et al. 2024

Energies

Self Cite

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The development of fractured oil fields poses a formidable challenge due to the intricate nature of fracture development and distribution. Fractures profoundly impact core resistivity, making it crucial to investigate the mechanism behind the resistivity response change in fracture cores. In this study, we employed the theory of a stable current field to perform a numerical simulation of the resistivity response of single-fracture and complex-fracture granite cores, using a full-size granite core with cracks as the model. We considered multiple parameters of the fracture itself and the formation to explore the resistivity response change mechanism of the fracture core. Our findings indicate that, in the case of a core with a single fracture, the angle, width, and length of the fracture (fracture occurrence) significantly affect core resistivity. When two fractures run parallel for a core with complex fractures, the change law of core resistivity is similar to that of a single fracture. However, if two fractures intersect, the relative position of the two fractures becomes a significant factor in addition to the width and length of the fracture. Interestingly, a 90° difference exists between the change law of core resistivity and the change law of the resistivity logging response. Furthermore, the core resistivity is affected by matrix resistivity and the resistivity of the mud filtrate, which emphasizes the need to calibrate the fracture dip angle calculated using dual laterolog resistivity with actual core data or special logging data in reservoirs with different geological backgrounds. In the face of multiple fractures, the dual laterolog method has multiple solutions. Our work provides a reference and theoretical basis for interpreting oil and gas in fractured reservoirs based on logging data and holds significant engineering guiding significance.

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A Method for Judging the Effectiveness of Complex Tight Gas Reservoirs Based on Geophysical Logging Data and Using the L Block of the Ordos Basin as a Case Study

Cited by 2 publications

References 29 publications

Lithofacies Identification from Wire-Line Logs Using an Unsupervised Data Clustering Algorithm

Lithofacies Identification from Wire-Line Logs Using an Unsupervised Data Clustering Algorithm

Numerical Simulation Study on the Influence of Cracks in a Full-Size Core on the Resistivity Measurement Response

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