Sembar Formation as an Unconventional Prospect: New Insights in Evaluating Shale Gas Potential Combined with Deep Learning

Amjad, Muhammad Raiees; Shakir, Urooj; Hussain, Muyyassar; Rasul, Arslan; Mehmood, Saqib; Ehsan, Muhsan

doi:10.1007/s11053-023-10244-x

Cited by 3 publications

(4 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Zamzama-02, the Pab Formation is interpreted as 223.5 m thick. The contact with the Khadro Formation is based on a major shale break. , However, it is dominated by pure sand in the lower part of the well. The Pab Formation is 230 m thick (measured depth) in Zamzama-05.…”

Section: Resultsmentioning

confidence: 99%

A Comparative Study Based on Petrophysical and Cluster Analysis Approach for Identification of Rock Types in Heterogeneous Sandstone Reservoirs

Munir,

Zafar,

Ali

et al. 2024

ACS Omega

Self Cite

View full text Add to dashboard Cite

To delineate a powerful reservoir model, rock type identification is an essential task. Recognizing intervals with promising reservoir quality in a heterogeneous reservoir, such as the Pab Formation, using well logs is critical for better exploration, because coring programs are always impractical due to time and cost constraints. Rock types are described by specific log responses, which are ultimately distinguished with the help of electrofacies. The current study uses a cluster analysis technique for the evaluation of reservoir rock types in the identified sand units. Kmeans cluster analysis is employed to define electrofacies, which are ultimately classified into four rock types on the basis of reservoir quality, from bad to excellent. Rock typing using cluster analysis has been done for four wells, and a correlation has been made to depict changes in electrofacies. From well-to-well correlation, it can be inferred that the reservoir quality of the Pab Formation at the lower portion of Zamzama-02 and 05 wells is excellent and is defined by rock type 4. The Zamzama-03 well in the southwestern region, on the other hand, has good to moderate reservoir quality, as demonstrated by dominating rock types 3 and 2, respectively. The applied prediction technique to the studied field provides continuous rock type identification for the entire reservoir. Using this methodology in defining rock type is cost-effective, requires less time in the demarcation of zones of interest, and is more accurate than manual analysis of the heterogeneous and thick Pab Formation. The studied approach is not only useful in the exploitation of the heterogeneous Pab Formation but also can be applied to other heterogeneous sandstone reservoirs elsewhere.

show abstract

Section: Resultsmentioning

confidence: 99%

A Comparative Study Based on Petrophysical and Cluster Analysis Approach for Identification of Rock Types in Heterogeneous Sandstone Reservoirs

Munir,

Zafar,

Ali

et al. 2024

ACS Omega

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nevertheless, to render this result quantitatively significant, it is imperative to have accurate data regarding the rock matrix and porosity. To overcome such limitations, a plethora of studies in recent years have progressively employed artificial intelligence techniques and machine-learning (ML) approaches to establish important relations between wireline log data and TOC for continuous TOC prediction with high accuracy. − Tan et al applied a support vector machine (SVM) for regression, using various kernel functions, to predict TOC values within gas-bearing shale from the Huangping syncline, China. This approach has encountered obstacles in enhancing the accuracy of the TOC content estimation.…”

Section: Introductionmentioning

confidence: 99%

“…An alternative approach, utilizing a data-driven binary SVM, was introduced to create a layer unit database properly labeled with core-measured TOC values to identify TOC-rich zones in cross-well applications within the Canadian Sedimentary Basin . Recently, a deep feedforward neural network has been applied to analyze seismic and well-log data to describe the spatial and vertical variations in the TOC content within Sembar shales . Hassan et al employed an artificial neural network to predict the TOC content using thermal neutron logs and delineating zones containing mature organic matter in the Horn River Formation.…”

Section: Introductionmentioning

confidence: 99%

“…8 Recently, a deep feedforward neural network has been applied to analyze seismic and well-log data to describe the spatial and vertical variations in the TOC content within Sembar shales. 9 Hassan et al 10 employed an artificial neural network to predict the TOC content using thermal neutron logs and delineating zones containing mature organic matter in the Horn River Formation. The potential for exploring innovative data-driven algorithms in TOC prediction, shale resource play evaluation, and feature characterization of influential variables, like bulk density, sonic slowness, gamma-ray, and others, for resource estimation is promising, because it has yet to be explored thus far.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Characterization and Feature Ranking of Well Log Variables Using Data-Driven Algorithms for Total Organic Carbon Estimation of Organic-Rich Shales

Kadri,

Ganguli,

Sen

et al. 2023

Energy Fuels

View full text Add to dashboard Cite

Reservoir rock typing for optimum permeability prediction of Nubia formation in October Field, Gulf of Suez, Egypt

Kassab,

Abbas,

Osman

et al. 2024

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

Permeability prediction and distribution is very critical for reservoir modeling process. The conventional method for obtaining permeability data is from cores, which is a very costly method. Therefore, it is usual to pay attention to logs for calculating permeability where it has massive limitations regarding this step. The aim of this study is to use unique artificial intelligence (AI) algorithms to tackle this challenge and predict permeability in the studied wells using conventional logs and routine core analysis results of the core plugs as an input to predict the permeability in non-cored intervals using extreme gradient boosting algorithm (XGB). This led to promising results as per the R2 correlation coefficient. The R2 correlation coefficient between the predicted and actual permeability was 0.73 when using the porosity measured from core plugs and 0.51 when using the porosity calculated from logs. This study presents the use of machine-learning extreme gradient boosting algorithm in permeability prediction. To our knowledge, this algorithm has not been used in this formation and field before. In addition, the machine-learning model established is uniquely simple and convenient as only four commonly available logs are required as inputs, it even provides reliable results even if one of the required logs for input is synthesized due to its unavailability.

show abstract

Sembar Formation as an Unconventional Prospect: New Insights in Evaluating Shale Gas Potential Combined with Deep Learning

Cited by 3 publications

References 67 publications

A Comparative Study Based on Petrophysical and Cluster Analysis Approach for Identification of Rock Types in Heterogeneous Sandstone Reservoirs

A Comparative Study Based on Petrophysical and Cluster Analysis Approach for Identification of Rock Types in Heterogeneous Sandstone Reservoirs

Characterization and Feature Ranking of Well Log Variables Using Data-Driven Algorithms for Total Organic Carbon Estimation of Organic-Rich Shales

Reservoir rock typing for optimum permeability prediction of Nubia formation in October Field, Gulf of Suez, Egypt

Contact Info

Product

Resources

About