Prediction of the Least Principal Stresses Using Drilling Data: A Machine Learning Application

• Published in: Computational intelligence and neuroscience 2021, Vol.2021 (1), p.8865827-8865827
• Main Authors: Gowida, Ahmed, Ibrahim, Ahmed Farid, Elkatatny, Salaheldin, Ali, Abdulwahab
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Correlation coefficients; Design optimization; Drilling; Drilling and boring; Drilling machines (tools); Empirical equations; Field study; Field tests; Learning algorithms; Learning theory; Machine Learning; Mathematical models; Neural networks; Neural Networks, Computer; Optimization; Predictions; Statistical analysis; Stress concentration; Stress state; Stresses; Tectonics; Values
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2021/8865827

The least principal stresses of downhole formations include minimum horizontal stress (σmin) and maximum horizontal stress (σmax). σmin and σmax are substantial parameters that significantly affect the design and optimization of the drilling process. These stresses can be estimated using theoretical equations in addition to some field tests, i.e., leak-off test to include the effect of tectonic stress. This approach is associated with many technical and financial issues. Therefore, the objective of this study is to provide a novel machine learning-based solution to estimate these stresses while drilling. First, new models were developed using artificial neural network (ANN) to directly predict σmin and σmax from the drilling data; which are injection rate (Q), standpipe pressure (SPP), weight on bit (WOB), torque (T), and rate of penetration (ROP). Such data are always available while drilling, and hence, no additional cost is required. Actual data from a Middle Eastern field were collected, statistically analyzed, and fed to the models. First, the models’ predictions showed a significant match with the actual stress values with a correlation coefficient (R-value) exceeding 0.90 and a mean absolute average error (MAPE) of 0.75% as a maximum. Second, new empirical equations were generated based on the developed ANN-based models. The new equations were then validated using another unseen dataset from the same field. The predictions had an R-value of 0.98 and 0.93 in addition to MAPE of 0.36% and 0.96% for σmin and σmax models, respectively. The results demonstrated the outperformance of the developed ANN-based equations to estimate the least principal stresses from the drilling data with high accuracy in a timely and economically effective way.