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Research Article
The Importance of the Use of Unmanned Aerial Vehicles (UAVs) in the Oil and Gas Industry
Taha Enes Kon*
Issue:
Volume 8, Issue 2, December 2024
Pages:
63-69
Received:
6 June 2024
Accepted:
5 July 2024
Published:
31 July 2024
Abstract: The oil and gas industry, renowned for its extensive and intricate operations, confronts numerous challenges in maintaining and inspecting its infrastructure. Traditional inspection methodologies, often reliant on manual labor, are not only time-consuming and costly but also expose workers to hazardous conditions. These conventional methods necessitate the shutdown of operations, leading to considerable productivity losses and elevated costs. Over the past decade, the advent of unmanned aerial vehicles (UAVs) has introduced a transformative technology in this sector, providing a safer, more efficient, and cost-effective alternative to traditional inspection techniques. UAVs, equipped with advanced sensors and high-resolution cameras, facilitate detailed visual and thermal inspections of critical assets such as pipelines, flare stacks, and offshore platforms. These capabilities enable companies to perform routine inspections without halting operations, thereby minimizing downtime and operational disruptions. The implementation of UAV technology has notably enhanced safety by reducing the need for human intervention in perilous environments. For instance, the traditional inspection of flare stacks necessitates workers to ascend these structures, posing significant risks. UAVs obviate this requirement by delivering real-time visual data from secure distances. Moreover, the precision and accuracy inherent in UAV inspections contribute to the early detection of defects and potential issues, allowing for prompt maintenance and repairs, which further augment safety and operational efficiency. The environmental benefits of UAV technology are also noteworthy. By diminishing the reliance on heavy machinery and extensive transportation typically associated with inspections and maintenance, UAVs aid in reducing carbon emissions, thus aligning with the industry's sustainability objectives. In summary, the integration of UAVs into the oil and gas industry's inspection protocols represents a significant technological advancement. This integration aligns with the sector's goals of enhancing safety, efficiency, and environmental sustainability, marking a pivotal step forward in the industry's evolution.
Abstract: The oil and gas industry, renowned for its extensive and intricate operations, confronts numerous challenges in maintaining and inspecting its infrastructure. Traditional inspection methodologies, often reliant on manual labor, are not only time-consuming and costly but also expose workers to hazardous conditions. These conventional methods necessi...
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Research Article
Simplified Neural Network-Based Models for Oil Flow Rate Prediction
Issue:
Volume 8, Issue 2, December 2024
Pages:
70-99
Received:
2 August 2024
Accepted:
9 September 2024
Published:
23 September 2024
DOI:
10.11648/j.pse.20240802.12
Downloads:
Views:
Abstract: Available neural network-based models for predicting the oil flow rate (qo) in the Niger Delta are not simplified and are developed from limited data sources. The reproducibility of these models is not feasible as the models’ details are not published. This study developed simplified and reproducible three, five, and six-input variables neural-based models for estimating qo using 283 datasets from 21 wells across fields in the Niger Delta. The neural-based models were developed using maximum-minimum (max.-min.) normalized and clip-normalized datasets. The performances and the generalizability of the developed models with published datasets were determined using some statistical indices: coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), average relative error (ARE) and average absolute relative error (AARE). The results indicate that the 3-input-based neural models had overall R2, MSE, and RMSE values of 0.9689, 9.6185x10-4 and 0.0310, respectively, for the max.-min. normalizing method and R2 of 0.9663, MSE of 5.7986x10-3 and RMSE of 0.0762 for the clip scaling approach. The 5-input-based models resulted in R2 of 0.9865, MSE of 5.7790×10-4 and RMSE of 0.0240 for the max.-min. scaling method and R2 of 0.9720, MSE of 3.7243x10-3 and RMSE of 0.0610 for the clip scaling approach. Also, the 6-input-based models had R2 of 0.9809, MSE of 8.7520x10-4 and RMSE of 0.0296 for the max.-min. normalizing approach and R2 of 0.9791, MSE of 3.8859 x 10-3 and RMSE of 0.0623 for the clip scaling method. Furthermore, the generality performance of the simplified neural-based models resulted in R2, RMSE, ARE, and AAPRE of 0.9644, 205.78, 0.0248, and 0.1275, respectively, for the 3-input-based neural model and R2 of 0.9264, RMSE of 2089.93, ARE of 0.1656 and AARE of 0.2267 for the 6-input-based neural model. The neural-based models predicted qo were more comparable to the test datasets than some existing correlations, as the predicted qo result was the lowest error indices. Besides, the overall relative importance of the neural-based models’ input variables on qo prediction is S>GLR>Pwh>T/Tsc>γo>BS&W>γg. The simplified neural-based models performed better than some empirical correlations from the assessment indicators. Therefore, the models should apply as tools for oil flow rate prediction in the Niger Delta fields, as the necessary details to implement the models are made visible.
Abstract: Available neural network-based models for predicting the oil flow rate (qo) in the Niger Delta are not simplified and are developed from limited data sources. The reproducibility of these models is not feasible as the models’ details are not published. This study developed simplified and reproducible three, five, and six-input variables neural-base...
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Review Article
Assessing the Rheological and Filtration Loss Control Potential of Selected Plant-Based Additives in Oil-Based Mud
Issue:
Volume 8, Issue 2, December 2024
Pages:
100-124
Received:
18 August 2024
Accepted:
4 September 2024
Published:
23 September 2024
DOI:
10.11648/j.pse.20240802.13
Downloads:
Views:
Abstract: In drilling operations, chemical additives pose environmental concerns during mud disposal. This study evaluated three plant-based additives, namely rice husk (RH), Detarium microcarpum (DM), and Brachystegia eurycoma (BE), in oil-based mud at low-pressure, low-temperature conditions. The mud’s rheological profile followed Herschel Bulkley’s model. With 8 g additive content, RH increased the mud's apparent viscosity (AV), plastic viscosity (PV), and yield point (YP) by 62.5%, 51.25%, and 34.38%, respectively. DM showed higher increases of 200.0%, 195.0%, and 162.5%, while BE exhibited the most significant improvements of 287.5%, 272.5%, and 250.0%. The filtration tests indicated that RH reduced spurt loss and fluid loss volumes by 83.33% and 62.35%, while DM decreased by 82.41% and 47.94%, as BE had the highest reduction of 94.44% and 51.18%. Again, the filter cake thickness of RH, DM, and BE muds increased by 210.29%, 273.53%, and 79.41%, respectively, with permeabilities of 8.90×10-3 mD, 11.87×10-3 mD, and 7.35×10-3 mD. Furthermore, the mud susceptibility to NaCl showed that AV decreased for RH, DM, and BE, while YP decreased significantly. The filter cake thickness and permeability increased by 62.38 and 359.55% for RH, as the DM decreased by 93.80% and 84.37% and the BE by 96.68% and 96.62%, which indicates that RH is more susceptible to NaCl than DM and BE in the mud. Also, these plant-based additives in mud exhibited fragile gel strength and commendable cake characteristics: firm, smooth, and soft/slippery, which make them potentially suitable for oil well drilling.
Abstract: In drilling operations, chemical additives pose environmental concerns during mud disposal. This study evaluated three plant-based additives, namely rice husk (RH), Detarium microcarpum (DM), and Brachystegia eurycoma (BE), in oil-based mud at low-pressure, low-temperature conditions. The mud’s rheological profile followed Herschel Bulkley’s model....
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