Application of Machine Learning Methods in Modeling the Loss of Circulation Rate while Drilling Operation.

Fluid losses into formations are a common operational issue that is frequently encountered when drilling across naturally or induced fractured formations. This could pose significant operational risks, such as well control, stuck pipe, and wellbore instability, which, in turn, lead to an increase in well time and cost. This research aims to use and evaluate different machine learning techniques, namely, support vector machine (SVM), random forest (RF), and K nearest neighbor (K-NN) in predicting the loss of circulation rate (LCR) while drilling using solely mechanical surface parameters and interpretation of the active pit volume readings. Actual field data of seven wells, which had suffered partial or severe loss of circulation, were used to build predictive models with an 80:20 training-to-test data ratio, while Well No. 8 was used to compare the performance of the developed models. Different performance metrics were used to evaluate the performance of the developed models. The root-mean-square error (RMSE) and correlation coefficient (R) were used to evaluate the performance of the models in predicting the LCR while drilling. The results showed that K-NN outperformed other models in predicting the LCR in Well No. 8 with an R of 0.90 and an RMSE of 0.17.

Intelligent Prediction for Rock Porosity While Drilling Complex Lithology in Real Time.

Rock porosity is an important parameter for the formation evaluation, reservoir modeling, and petroleum reserve estimation. The conventional methods for determining the rock porosity are considered costly and time-consuming operations during the well drilling. This paper aims to predict the rock porosity in real time while drilling complex lithology using machine learning. In this paper, two intelligent models were developed utilizing the random forest (RF) and decision tree (DT) techniques. The drilling parameters include weight on bit, torque, standpipe pressure, drill string rotation speed, rate of penetration, and pump rate. Two datasets were employed for building the models (3767 data points) and for validating the developed models (1676 data points). Both collected datasets have complex lithology of carbonate, sandstone, and shale. Sensitivity and optimization on different parameters for each technique were conducted to ensure optimum prediction. The models' performance was checked by four performance indices which are coefficient of determination (R 2), average absolute percentage error (AAPE), variance account for (VAF), and a20 index. The results indicated the strong porosity prediction capability for the two models. DT model showed R 2 of 0.94 and 0.87 between the predicted and actual porosity values with AAPE of 6.07 and 9% for training and testing, respectively. Generally, RF provided a higher level of strong prediction than DT as RF achieved R 2 of 0.99 and 0.90 with AAPE of 1.5 and 7% for training and testing, respectively. The models' validation proved a high prediction performance as DT achieved R 2 of 0.88 and AAPE of 8.58%, while RF has R 2 of 0.92 and AAPE of 6.5%.

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

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.

Real-Time Prediction of Equivalent Circulation Density for Horizontal Wells Using Intelligent Machines.

Equivalent circulation density (ECD) is an important part of drilling fluid calculations. Analytical equations based on the conservation of mass and momentum are used to determine the ECD at various depths in the wellbore. However, these equations do not incorporate important factors that have a direct impact on the ECD, such as bottom-hole temperature, pipe rotation and eccentricity, and wellbore roughness. This work introduced different intelligent machines that could provide a real-time accurate estimation of the ECD for horizontal wells, namely, the support vector machine (SVM), random forests (RF), and a functional network (FN). Also, this study sheds light on how principal component analysis (PCA) can be used to reduce the dimensionality of a data set without loss of any important information. Actual field data of Well-1, including drilling surface parameters and ECD measurements, were collected from a 5-7/8 in. horizontal section to develop the models. The performance of the models was assessed in terms of root-mean-square error (RMSE) and coefficient of determination (R 2). Then, the best model was validated using unseen data points of 1152 collected from Well-2. The results showed that the RF model outperformed the FN and SVM in predicting the ECD with an RMSE of 0.23 and R 2 of 0.99 in the training set and with an RMSE of 0.42 and R 2 of 0.99 in the testing set. Furthermore, the RF predicted the ECD in Well-2 with an RMSE of 0.35 and R 2 of 0.95. The developed models will help the drilling crew to have a comprehensive view of the ECD while drilling high-pressure high-temperature wells and detect downhole operational issues such as poor hole cleaning, kicks, and formation losses in a timely manner. Furthermore, it will promote safer operation and improve the crew response time limit to prevent undesired events.