New Approach to Optimize the Rate of Penetration Using Artificial Neural Network

The Application Potential of Artificial Intelligence and Numerical Simulation in the Research and Formulation Design of Drilling Fluid Gel Performance

Drilling fluid is pivotal for efficient drilling. However, the gelation performance of drilling fluids is influenced by various complex factors, and traditional methods are inefficient and costly. Artificial intelligence and numerical simulation technologies have become transformative tools in various disciplines. This work reviews the application of four artificial intelligence techniques-expert systems, artificial neural networks (ANNs), support vector machines (SVMs), and genetic algorithms-and three numerical simulation techniques-computational fluid dynamics (CFD) simulations, molecular dynamics (MD) simulations, and Monte Carlo simulations-in drilling fluid design and performance optimization. It analyzes the current issues in these studies, pointing out that challenges in applying these two technologies to drilling fluid gelation performance research include difficulties in obtaining field data and overly idealized model assumptions. From the literature review, it can be estimated that 52.0% of the papers are related to ANNs. Leakage issues are the primary concern for practitioners studying drilling fluid gelation performance, accounting for over 17% of research in this area. Based on this, and in conjunction with the technical requirements of drilling fluids and the development needs of drilling intelligence theory, three development directions are proposed: (1) Emphasize feature engineering and data preprocessing to explore the application potential of interpretable artificial intelligence. (2) Establish channels for open access to data or large-scale oil and gas field databases. (3) Conduct in-depth numerical simulation research focusing on the microscopic details of the spatial network structure of drilling fluids, reducing or even eliminating data dependence.

Mining Technology Evaluation for Steep Coal Seams Based on a GA-BP Neural Network

Many mines in Guizhou Province are in urgent need of renovation to ensure harmonious operation and prolong their lifespan. The key to successful renovation lies in the prudent selection of the appropriate mining technologies. Therefore, a comprehensive investigation was conducted on steep coal mines in Guizhou Province, and a comprehensive evaluation framework was established. Spearman correlation analysis was performed on various factors, selecting geological conditions and working face parameters with high correlation as the input variables and mining methods as the output variables. The optimal values of each hyperparameter were determined through orthogonal experiments, and the neural network structure was confirmed to be "17-9-3". Five variants of backpropagation (BP) algorithms were meticulously tested, and a genetic algorithm optimizing the BP neural network (GA-BP) was further assessed to improve the model's prediction accuracy. The accuracy of the model was evaluated via the coefficient of determination (R 2) and mean squared error (MSE). The research results indicated that the variable step-size algorithm with a momentum term (VSS + MT) was the optimal algorithm for the BP neural network. Additionally, the MSE values of the artificial neural network and GA-BP neural network in the testing phase were 0.06 and 0.04, with prediction success rates of 70 and 90%, respectively, and R 2 values of 0.79 and 0.85, respectively. Thus, the GA-BP neural network demonstrated superior performance. Finally, industrial application of the model was conducted on a working face in the Zhong-Yu coal mine. The evaluation index for the working face was "0.847, 0.09, 0.111", suggesting that fully mechanized mining should be adopted. The evaluation results were consistent with the current production status of the mine, verifying the reliability of the model in practical applications.

Applications of Different Classification Machine Learning Techniques to Predict Formation Tops and Lithology While Drilling

Accurate prediction of formation tops and lithology plays a critical role in optimizing drilling processes, cost reduction, and risk mitigation in hydrocarbon operations. Although several techniques like well logging, core sampling, cuttings analysis, seismic surveys, and mud logging are available for identifying formation tops, they have limitations such as high costs, lower accuracy, manpower-intensive processes, and time or depth lags that impede real-time estimation. Consequently, this study aims to leverage machine learning models based on easily accessible drilling parameters to predict formation tops and lithologies, overcoming the limitations associated with traditional methods. Data from two wells (A and B) in the Middle East, encompassing drilling mechanical parameters such as rate of penetration (ROP), drill string rotation (DSR), pumping rate (Q), standpipe pressure (SPP), weight on bit (WOB), and torque, were collected for real-field analysis. Machine learning models including Gaussian naive Bayes (GNB), logistic regression (LR), and linear discriminant analysis (LDA) were trained and tested on the data set from well A, while the data set from well B was utilized for model validation as unseen data. The formations of wells A and B consist of four lithologies, namely, sandstone, anhydrite, carbonate/shale, and carbonates, necessitating the development of multiclass classification models. The drilling parameters, specifically the WOB and ROP, exhibited a strong influence on lithology identification. Among the models, GNB demonstrated exceptional performance in predicting formation lithology from the drilling parameters, achieving accuracy and nearly perfect precision, recall, and F1 score for the different classes. LDA and LR models accurately predicted sandstone and carbonate lithologies, although some misclassifications occurred in approximately 5% of points for anhydrite and around 20% in carbonate/shale formations. During validation, the models demonstrated accuracies of around 0.96, 0.95, and 0.92 for the GNB, LR, and LDA, respectively. The study highlights the efficacy of the developed machine learning models in accurately predicting the formation lithology and tops in real time. This is achieved by utilizing readily available drilling parameters, making the approach highly accurate and cost effective by leveraging existing real-time drilling data.

Real-Time Prediction of Rate of Penetration in S-Shape Well Profile Using Artificial Intelligence Models

Rate of penetration (ROP) is defined as the amount of removed rock per unit area per unit time. It is affected by several factors which are inseparable. Current established models for determining the ROP include the basic mathematical and physics equations, as well as the use of empirical correlations. Given the complexity of the drilling process, the use of artificial intelligence (AI) has been a game changer because most of the unknown parameters can now be accounted for entirely at the modeling process. The objective of this paper is to evaluate the ability of the optimized adaptive neuro-fuzzy inference system (ANFIS), functional neural networks (FN), random forests (RF), and support vector machine (SVM) models to predict the ROP in real time from the drilling parameters in the S-shape well profile, for the first time, based on the drilling parameters of weight on bit (WOB), drillstring rotation (DSR), torque (T), pumping rate (GPM), and standpipe pressure (SPP). Data from two wells were used for training and testing (Well A and Well B with 4012 and 1717 data points, respectively), and one well for validation (Well C) with 2500 data points. Well A and Well B data were combined in the training-testing phase and were randomly divided into a 70:30 ratio for training/testing. The results showed that the ANFIS, FN, and RF models could effectively predict the ROP from the drilling parameters in the S-shape well profile, while the accuracy of the SVM model was very low. The ANFIS, FN, and RF models predicted the ROP for the training data with average absolute percentage errors (AAPEs) of 9.50%, 13.44%, and 3.25%, respectively. For the testing data, the ANFIS, FN, and RF models predicted the ROP with AAPEs of 9.57%, 11.20%, and 8.37%, respectively. The ANFIS, FN, and RF models overperformed the available empirical correlations for ROP prediction. The ANFIS model estimated the ROP for the validation data with an AAPE of 9.06%, whereas the FN model predicted the ROP with an AAPE of 10.48%, and the RF model predicted the ROP with an AAPE of 10.43%. The SVM model predicted the ROP for the validation data with a very high AAPE of 30.05% and all empirical correlations predicted the ROP with AAPEs greater than 25%.

Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks

High-bentonite mud (HBM) is a water-based drilling fluid characterized by its remarkable improvement in cutting removal and hole cleaning efficiency. Periodic monitoring of the rheological properties of HBM is mandatory for optimizing the drilling operation. The objective of this study is to develop new sets of correlations using artificial neural network (ANN) to predict the rheological parameters of HBM while drilling using the frequent measurements, every 15 to 20 min, of mud density (MD) and Marsh funnel viscosity (FV). The ANN models were developed using 200 field data points. The dataset was divided into 70:30 ratios for training and testing the ANN models respectively. The optimized ANN models showed a significant match between the predicted and the measured rheological properties with a high correlation coefficient (R) higher than 0.90 and a maximum average absolute percentage error (AAPE) of 6%. New empirical correlations were extracted from the ANN models to estimate plastic viscosity (PV), yield point (Y_P), and apparent viscosity (AV) directly without running the models for easier and practical application. The results obtained from AV empirical correlation outperformed the previously published correlations in terms of R and AAPE.

A New Model for Predicting Rate of Penetration Using an Artificial Neural Network

The drilling rate of penetration (ROP) is defined as the speed of drilling through rock under the bit. ROP is affected by different interconnected factors, which makes it very difficult to infer the mutual effect of each individual parameter. A robust ROP is required to understand the complexity of the drilling process. Therefore, an artificial neural network (ANN) is used to predict ROP and capture the effect of the changes in the drilling parameters. Field data (4525 points) from three vertical onshore wells drilled in the same formation using the same conventional bottom hole assembly were used to train, test, and validate the ANN model. Data from Well A (1528 points) were utilized to train and test the model with a 70/30 data ratio. Data from Well B and Well C were used to test the model. An empirical equation was derived based on the weights and biases of the optimized ANN model and compared with four ROP models using the data set of Well C. The developed ANN model accurately predicted the ROP with a correlation coefficient (R) of 0.94 and an average absolute percentage error (AAPE) of 8.6%. The developed ANN model outperformed four existing models with the lowest AAPE and highest R value.

Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique

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.