Rate of penetration (ROP) is one of the most important drilling parameters for optimizing the cost of drilling hydrocarbon wells. In this study, a new empirical correlation based on an optimized artificial neural network (ANN) model was developed to predict ROP alongside horizontal drilling of carbonate reservoirs as a function of drilling parameters, such as rotation speed, torque, and weight-on-bit, combined with conventional well logs, including gamma-ray, deep resistivity, and formation bulk density. The ANN model was trained using 3000 data points collected from Well-A and optimized using the self-adaptive differential evolution (SaDE) algorithm. The optimized ANN model predicted ROP for the training dataset with an average absolute percentage error (AAPE) of 5.12% and a correlation coefficient (R) of 0.960. A new empirical correlation for ROP was developed based on the weights and biases of the optimized ANN model. The developed correlation was tested on another dataset collected from Well-A, where it predicted ROP with AAPE and R values of 5.80% and 0.951, respectively. The developed correlation was then validated using unseen data collected from Well-B, where it predicted ROP with an AAPE of 5.29% and a high R of 0.956. The ANN-based correlation outperformed all previous correlations of ROP estimation that were developed based on linear regression, including a recent model developed by Osgouei that predicted the ROP for the validation data with a high AAPE of 14.60% and a low R of 0.629.
Drilling deep and high-pressure high-temperature wells have many challenges and problems. One of the most severe, costly and time-consuming problem in the drilling operation is the loss of circulation. The drilling fluid accounts for 25-40% of the total cost of the drilling operation. Any loss of the drilling fluid will increase the total cost of the drilling operation. Uncontrolled lost circulation of the drilling fluid can result in dangerous well control problems and in some cases the loss of the well. In order to avoid loss circulation, many methods were introduced to identify the zones of losses. However, some of these methods are difficult to be applied due to financial issues and lack of technology and the other methods are not accurate in the prediction of the thief zones. The objective of this paper is to predict the lost circulation zones using two different techniques of artificial intelligence (AI). More than 5000 real field data from two wells that contain the real-time surface drilling parameters was used to predict the zones of circulation loss using radial basis function (RBF) and support vector machine (SVM). Six surface drilling parameters were used as an input. The data of the well (A) was divided into training and testing to build the two AI models and then unseen well (B) was used to validate the ability of AI models to predict the zones of lost circulation. The obtained results showed that the two models of AI were able to predict the zones of circulation loss in well (A) with high accuracy in terms of correlation coefficient (R), root mean squared error (RMSE) and confusion matrix. RBF predicted the losses zones with excellent precision of R = 0.981 and RMSE = 0.088. SVM also achieved higher accuracy with a correlation coefficient of 0.997 and root mean squared error of 0.038. Moreover, the RBF model was able to predict the losses zones in the unseen well (B) that was used as a validation for the ability of the AI models with a correlation coefficient of 0.909 and root mean squared error of 1.686.
Drilling a high-pressure, high-temperature (HPHT) well involves many difficulties and challenges. One of the greatest difficulties is the loss of circulation. Almost 40% of the drilling cost is attributed to the drilling fluid, so the loss of the fluid considerably increases the total drilling cost. There are several approaches to avoid loss of return; one of these approaches is preventing the occurrence of the losses by identifying the lost circulation zones. Most of these approaches are difficult to apply due to some constraints in the field. The purpose of this work is to apply three artificial intelligence (AI) techniques, namely, functional networks (FN), artificial neural networks (ANN), and fuzzy logic (FL), to identify the lost circulation zones. Real-time surface drilling parameters of three wells were obtained using real-time drilling sensors. Well A was utilized for training and testing the three developed AI models, whereas Well B and Well C were utilized to validate them. High accuracy was achieved by the three AI models based on the root mean square error (RMSE), confusion matrix, and correlation coefficient (R). All the AI models identified the lost circulation zones in Well A with high accuracy where the R is more than 0.98 and RMSE is less than 0.09. ANN is the most accurate model with R=0.99 and RMSE=0.05. An ANN was able to predict the lost circulation zones in the unseen Well B and Well C with R=0.946 and RMSE=0.165 and R=0.952 and RMSE=0.155, respectively.
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