Applications of Artificial Intelligence for Static Poisson’s Ratio Prediction While Drilling

Abstract

The prediction of continued profile for static Poisson’s ratio is quite expensive and requires huge experimental works, and the discontinuity in the measurement and the limited applicability and accuracy of the present empirical correlations necessitated the utilization of artificial intelligence with its prosperous application in oil and gas industry. This work aims to construct different artificial intelligence models for predicting static Poisson’s ratio of complex lithology at real time during drilling. The functional networks (FN) and random forest (RF) approaches were utilized using the mechanical drilling parameters as inputs. This study uses a vertical well with 1775 records from complex lithology containing shale, sand, and carbonate for model building. Besides, a different dataset from another well was used to check the models’ validity. The results demonstrated that both FN- and RF-based models predicted static Poisson’s ratio with significant matching accuracy. The FN technique results’ correlation coefficient (R) value of 0.89 and average absolute percentage error (AAPE) values of 10.23% and 10.28% in training and testing processes. While the RF technique is outperformed, as illustrated by the highest R values of 0.99 and 0.94 and the lowest AAPE values of 1.89% and 5.19% for training and testing processes, the robustness and reliability of the developed models were confirmed in the validation process with R values of 0.94 and 0.86 and AAPE values of 11.23% and 5.12% for FN- and RF-based models, respectively. The constructed models developed a basis for inexpensive static Poisson’s ratio prediction in real time with significant accuracy.

A Survey on Industry 4.0 for the Oil and Gas Industry: Upstream Sector

“…This helps to increase the productive time of drilling in O&G wells. The use of AI has been adopted to improve the accuracy of prediction of rock characteristics which are determined by elastic parameters like Poisson's ratio and Young's modulus [130]- [133]. Such accuracy minimizes the risk associated with well drilling operations.…”

Applied machine learning-based models for predicting the geomechanical parameters using logging data

“…Khatibi and Aghajanpour (2020) applied machine learning to estimate in situ stresses and GM parameters from offshore gas reservoir information. Ahmed et al (2021) estimated the Poisson ratio by applying machine learning methods using drilling parameters. Aghakhani Emamqeysi et al (2023) predicted the elastic parameters in gas reservoirs using the ensemble method.…”

Real-time prediction of formation pressure gradient while drilling

“…AI has been broadly applied in oil and gas industry because it has not only the capability to solve complicated issues, but it also represents them with a high accuracy 27 . Intelligent models were developed for various targets such as estimating the equivalent circulation density in real-time 28 – 30 , pore pressure estimation while drilling 31 , 32 , porosity prediction 33 , resistivity prediction 34 , predicting mud rheological properties 35 – 39 , predicting the unconfined compressive strength 40 , estimating the oil recovery factor 41 , bulk density log prediction 42 , 43 , well planning 44 , lithology classification 45 , fracture density estimation 46 , estimating the static elastic moduli 47 , 48 , Poisson’s ratio prediction 49 – 51 , and prediction of formation tops 52 .…”