Application of neural networks to traveltime computation

Kononov, Alexey; Gisolf, Dries; Verschuur, Eric

doi:10.1190/1.2792838

Cited by 6 publications

(1 citation statement)

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“…The AI techniques became a hotspot in petroleum industry since the early nineties and applied in different aspects [ 10 , 11 ]. The AI was applied in exploration to predict the feature recognition [ 12 ], travel time calculation [ 13 , 14 ], and clarity and resolution of seismic data enhancement [ 15 ]. Several AI models were applied in drilling aspects such as operations monitoring, data correction, and prediction of drilling parameters and mud properties [ 16 – 25 ].…”

Section: Introductionmentioning

confidence: 99%

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

Ahmed

Elkatatny

Alsaihati

2021

Computational Intelligence and Neuroscience

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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.

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