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Issa, Mustafa Adil, Al-Zuobaidi, Ali A., Al-kanaani, Nuhad A., Issa, Muntadher Adil, Hadi, Farqad Ali. (1404). A Comparative Study on the Prediction of Unconfined Compressive Strength for the Sandstone Formations Based on Well Logging Data. , (), -. doi: 10.22077/jgm.2025.10315.1062
Mustafa Adil Issa; Ali A. Al-Zuobaidi; Nuhad A. Al-kanaani; Muntadher Adil Issa; Farqad Ali Hadi. "A Comparative Study on the Prediction of Unconfined Compressive Strength for the Sandstone Formations Based on Well Logging Data". , , , 1404, -. doi: 10.22077/jgm.2025.10315.1062
Issa, Mustafa Adil, Al-Zuobaidi, Ali A., Al-kanaani, Nuhad A., Issa, Muntadher Adil, Hadi, Farqad Ali. (1404). 'A Comparative Study on the Prediction of Unconfined Compressive Strength for the Sandstone Formations Based on Well Logging Data', , (), pp. -. doi: 10.22077/jgm.2025.10315.1062
Issa, Mustafa Adil, Al-Zuobaidi, Ali A., Al-kanaani, Nuhad A., Issa, Muntadher Adil, Hadi, Farqad Ali. A Comparative Study on the Prediction of Unconfined Compressive Strength for the Sandstone Formations Based on Well Logging Data. , 1404; (): -. doi: 10.22077/jgm.2025.10315.1062

A Comparative Study on the Prediction of Unconfined Compressive Strength for the Sandstone Formations Based on Well Logging Data

Journal of Geomine
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 17 آذر 1404
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22077/jgm.2025.10315.1062
نویسندگان
Mustafa Adil Issa* 1؛ Ali A. Al-Zuobaidi2؛ Nuhad A. Al-kanaani3؛ Muntadher Adil Issa1؛ Farqad Ali Hadi4
1Petroleum Engineer
2Engineer at Basra Oil Company, Basra, Iraq
3Dr. at the Oil and Gas Engineering Department, University of Basra, Basra, Iraq
4Assistant Professor at the Petroleum Engineering Department, University of Baghdad, Baghdad, Iraq
چکیده
Unconfined Compressive Strength (UCS) is a crucial geomechanical parameter commonly used in petroleum engineering and geology to evaluate rock integrity and its response to stress. Therefore, accurate measurements or estimations of UCS values are essential for efficient reservoir management and operational planning due to their significant role in evaluating wellbore stability, constructing fracture stimulation, and implementing well control techniques. However, to precisely ascertain the values of UCS, it is important to acquire rock samples from the specified region of interest. Unfortunately, the retrieved cores typically only extend to the reservoir section and exhibit significant discontinuities. Moreover, the core extraction process is both time-consuming and costly. Therefore, to determine the UCS profile for the study region, it is essential to choose the most appropriate correlation. As a result, this study commenced, and a field case study in southern Iraq was conducted to construct dependable and uncomplicated mathematical models, i.e., multiple regression analysis (MRA) and artificial neural networks (ANN), for the sandstone formations to generate the UCS profile based on well-logging data. The results demonstrate that both the ANN and MRA models effectively predict the UCS when compared to the empirical correlations found in the literature and actual UCS values. Furthermore, the ANN model shows superior performance over the MRA model, achieving a higher determination coefficient of 0.99, compared to the MRA's coefficient of 0.84. Finally, this study presents efficient and cost-effective methods that integrate traditional well logs to predict the UCS profile.
کلیدواژه‌ها
Mechanical Rock Properties؛ UCS؛ ANN؛ Multiple Regression Analysis؛ Well logging
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