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Research Article
Uncertainty Analysis of Hydrocarbon Estimation Using 3D Geological Model: A Case Study of the F3 Sandstone Reservoir, Ghadames Basin, Libya
Issue:
Volume 9, Issue 1, June 2025
Pages:
1-12
Received:
10 October 2024
Accepted:
3 December 2024
Published:
20 February 2025
Abstract: The Wafa oil and gas field, located in Libya's Ghadames Basin, has historically faced challenges in accurately characterizing its F3 Sandstone Reservoir due to limited structural and stratigraphic data, particularly in certain wells. This study addresses these gaps by employing a comprehensive 3D geological model to evaluate the reservoir's structural and petrophysical properties across five newly drilled wells. Utilizing Petrel software, geological and petrophysical models were developed, with porosity and water saturation values derived from well log data processed in Techlog software. Results reveal significant heterogeneity in reservoir properties, with porosity levels ranging from 12% to 17% in most wells, while well A12 exhibits negligible porosity due to lithological variations. Water saturation levels range from 30% to 45% in the northwest, increasing to 100% in the southern well A12. The structural model highlights the potential for oil trapping in the F3 reservoir, with distinct facies and petrophysical variations observed across the study area. Rock typing analysis further underscores the heterogeneity within the F3 members, with the northern region demonstrating favorable petrophysical characteristics and porosity, contrasting with the less promising southern region. This study provides critical insights into the geological and petrophysical properties of the F3 Sandstone Reservoir, offering valuable guidance for hydrocarbon exploration and production in the Ghadames Basin. The integration of 3D geological modeling and petrophysical analysis underscores the need for high-resolution seismic data to enhance reservoir characterization accuracy, thereby supporting informed decision-making for future exploration and development efforts.
Abstract: The Wafa oil and gas field, located in Libya's Ghadames Basin, has historically faced challenges in accurately characterizing its F3 Sandstone Reservoir due to limited structural and stratigraphic data, particularly in certain wells. This study addresses these gaps by employing a comprehensive 3D geological model to evaluate the reservoir's structu...
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Research Article
Development of Maximum Efficient Rate Model and Improvement of Erosional Velocity-Based Correlation for Vertical Oil Wells in the Niger Delta
Issue:
Volume 9, Issue 1, June 2025
Pages:
13-21
Received:
10 March 2025
Accepted:
31 March 2025
Published:
27 April 2025
Abstract: Maximum efficient rate (MER) and erosional velocity are known to be vital concepts in oil and gas production, and producing a well at a maximum efficient rate remains a critical concern to the oil and gas operators, the production engineer and the regulator. Well testing and equilibrium concept are commonly used by oil and gas players to determine the MER of a well. However, little adjustment of the plot axes of the production rate, choke size and tubing head pressure can affect the accuracy of the MER determination. Additionally, there are no known generalized correlations to compare results with that of the MER tests. Furthermore, oil regulatory bodies in Nigeria have no known published models for estimating the technical allowable rate, unlike other regulatory bodies in other countries. This work therefore presents the outcomes of the formulation of MER and the improved erosional velocity-based correlations for vertical oil wells, using MER test data from the Niger Delta region of Nigeria. Multiple linear regression (MLR) and probabilistic modeling approaches were considered. The predicted normalized MER results compared favorably well with the MER test data, with an absolute average error of 7.62%. For the case examples, de-normalization of the predicted MER results increases the absolute average error. Among the predicted P10, P50 and P90 MER results, the predicted P10 results are the nearest to the MER test results. Improvement in the predicted probabilistic results depends on the mean value of the predicted normalized MER considered. The combination of the MER model and the improved erosional velocity-based correlation can be a useful tool for MER test results verification and determination, and in overall for optimization of oil wells.
Abstract: Maximum efficient rate (MER) and erosional velocity are known to be vital concepts in oil and gas production, and producing a well at a maximum efficient rate remains a critical concern to the oil and gas operators, the production engineer and the regulator. Well testing and equilibrium concept are commonly used by oil and gas players to determine ...
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Case Report
Calibrated 3D Geomechanical Model for Forecasting Gas Storage Seismicity
Cas Berentsen*
,
Hans De Pater
Issue:
Volume 9, Issue 1, June 2025
Pages:
22-37
Received:
3 March 2025
Accepted:
14 March 2025
Published:
29 April 2025
Abstract: The Bergermeer reservoir is one of the largest gas storages in Western-Europe, with excellent reservoir properties. However, during original depletion seismicity was observed with magnitude up to 3.5 at a pressure below 58 bar. Therefore, it was deemed prudent to monitor the gas storage reservoir with a permanent, downhole micro-seismic array. The monitoring is embedded in a so-called Traffic Light System for managing seismic activity. The array observed 400 micro-seismic events with a magnitude below 0.9 during refill of the reservoir. Most activity was induced by the Midfield fault that also induced depletion seismicity. This activity is far below the threshold for felt or damaging earthquakes. A calibrated geomechanical model was developed that matches the micro-seismicity observed during refill and storage cycles. The geometry was obtained from the seismic interpretations using an efficient method to convert the faults and horizons to parametric surfaces that can be used in a Finite Element Method (FEM) model. The model was populated with Leak-off Test (LOT), minifrac, core and log data. The model was calibrated on observed depletion and refill seismicity, stress measurements and the observed surface displacement from geodetic and GPS surveys. The continuous records of surface displacements showed a much stiffer reservoir during refill, compared with depletion. Also, the response was delayed by 0.25 year with respect to pressure, indicating time dependence in the rock deformation. Fault slip during depletion and stress hysteresis explains the refill micro-seismicity, since the peak shear stress is redistributed during slippage, giving higher shear stress at the edges of the slip area. The model predicts higher seismic activity for higher injection rate, but the maximum magnitude is limited in the worst case to 2.2, which is expected to cause no damage.
Abstract: The Bergermeer reservoir is one of the largest gas storages in Western-Europe, with excellent reservoir properties. However, during original depletion seismicity was observed with magnitude up to 3.5 at a pressure below 58 bar. Therefore, it was deemed prudent to monitor the gas storage reservoir with a permanent, downhole micro-seismic array. The ...
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Research Article
Application of Machine Learning for Bit-formation Matching in Drilling Operations
Issue:
Volume 9, Issue 1, June 2025
Pages:
38-47
Received:
9 April 2025
Accepted:
27 April 2025
Published:
29 May 2025
Abstract: Efficient bit formation matching is imperative for the success and cost-effectiveness of drilling operations and emissions reduction to provide energy solutions. Currently, drill bit selection predominantly depends on historical data and experiential knowledge. While machine learning, particularly Artificial Neural Networks (ANNs), has gained prominence in bit selection, other diverse and impactful algorithms such as XGBOOST and Random Forest (RF), are often overlooked. This paper involves the systematic application and comparative analysis of XGBOOST, RF, and ANN, alongside an optimization approach using Genetic Algorithm. The study comprehensively considers various influential factors including formation properties, drilling fluid characteristics, bit design, and operational parameters. In this study, we achieved promising results with the highest classification accuracy for bit selection recorded at 0.97 using the XGBOOST model, while RF and ANN yielded accuracies of 0.91 and 0.93 respectively. Additionally, we obtained impressive R squared values of 0.991, 0.975, and 0.953 for predicting the Rate of Penetration using the XGBOOST, ANN, and RF models respectively. These algorithms, coupled with the optimization techniques, aim to establish a robust framework for nuanced and accurate bit-formation matching. The results obtained hold significant potential for minimizing costs and optimizing resource allocation and utilization during the planning and execution of drilling projects in the oil and gas industry.
Abstract: Efficient bit formation matching is imperative for the success and cost-effectiveness of drilling operations and emissions reduction to provide energy solutions. Currently, drill bit selection predominantly depends on historical data and experiential knowledge. While machine learning, particularly Artificial Neural Networks (ANNs), has gained promi...
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