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Fundamentals of drilling engineering

Real-time predictive capabilities of analytical and machine learning rate of penetration (ROP) models

Machine learning methods applied to drilling rate of penetration prediction and optimization - A review


 Oil/gas exploration, drilling, production, and reservoir management are challenging these days since most oil and gas conventional sources are already discovered and have been producing for many years. That is why petroleum engineers are trying to use advanced tools such as artificial neural networks (ANNs) to help to make the decision to reduce non-productive time and cost.
 A good number of papers about the applications of ANNs in the petroleum literature were reviewed and summarized in tables. The applications were classified into four groups; applications of ANNs in explorations, drilling, production, and reservoir engineering. A good number of applications in the literature of petroleum engineering were tabulated. Also, a formalized methodology to apply the ANNs for any petroleum application was presented and accomplished by a flowchart that can serve as a practical reference to apply the ANNs for any petroleum application. The method was broken down into steps that can be followed easily. The availability of huge data sets in the petroleum industry gives the opportunity to use these data to make better decisions and predict future outcomes. This paper will provide a review of applications of ANNs in petroleum engineering as well as a clear methodology on how to apply the ANNs for any petroleum application.

Applications of Artificial Neural Networks in the Petroleum Industry: A Review

Rate of penetration (ROP) is one of the most important parameters of the drilling operation. Optimizing the ROP will reduce the overall cost of the drilling process. ROP depends on many variables such as drilling parameters [flow rate (Q), RPM, torque (T), weight on bit (WOB), stand pipe pressure (P)], fluid properties (mud density and plastic viscosity), and formation strength (UCS). The objectives of this paper are (1) to evaluate the effect of the drilling parameters and the drilling fluid parameters on rate of penetration (ROP), (2) construct a new artificial neural network (ANN) technique to estimate the ROP as a function of drilling parameters and fluid properties using actual field measurements (3333 data points), and (3) converting the black box of the ANN model to a white box by developing a new ROP correlation based on the optimized weights and biases of the developed model. The optimization process of the ANN model showed that the optimum number of neurons was 20, which resulted in the lowest average absolute percentage error (AAPE) and the highest correlation coefficient (R). The developed ANN model was able to estimate ROP with high accuracy (R of 0.99 and AAPE of 5.6%). The developed empirical correlation for ROP prediction outperformed the previous models. The high accuracy of the developed correlation (AAPE of 4%) confirmed the importance of compiling the drilling parameters and the drilling fluid properties.

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

During the drilling operations, optimizing the rate of penetration (ROP) is very crucial, because it can significantly reduce the overall cost of the drilling process. ROP is defined as the speed at which the drill bit breaks the rock to deepen the hole, and it is measured in units of feet per hour or meters per hour. ROP prediction is very challenging before drilling, because it depends on many parameters that should be optimized. Several models have been developed in the literature to predict ROP. Most of the developed models used drilling parameters such as weight on bit (WOB), pumping rate (Q), and string revolutions per minute (RPM). Few researchers considered the effect of mud properties on ROP by including a small number of actual field measurements. This paper introduces a new robust model to predict the ROP using both drilling parameters (WOB, Q, ROP, torque (T), standpipe pressure (SPP), uniaxial compressive strength (UCS), and mud properties (density and viscosity) using 7000 real-time data measurements. In addition, the relative importance of drilling fluid properties, rock strength, and drilling parameters to ROP is determined. The obtained results showed that the ROP is highly affected by WOB, RPM, T, and horsepower (HP), where the coefficient of determination (T2) was 0.71, 0.87, 0.70, and 0.92 for WOB, RPM, T, and HP, respectively. ROP also showed a strong function of mud fluid properties, where R2 was 0.70 and 0.70 for plastic viscosity (PV) and mud density, respectively. No clear relationship was observed between ROP and yield point (YP) for more than 500 field data points. The new model predicts the ROP with average absolute percentage error (AAPE) of 5% and correlation coefficient (R) of 0.93. In addition, the new model outperformed three existing ROP models. The novelty in this paper is the application of the clustering technique in which the formations are clustered based on their compressive strength range to predict the ROP. Clustering yielded accurate ROP prediction compared to the field ROP.

A Robust Rate of Penetration Model for Carbonate Formation

Optimization of Rate of Penetration using Artificial Intelligent Techniques

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.

Ahmad Al-AbdulJabbar

Papers

A Robust Rate of Penetration Model for Carbonate Formation

Optimization of Rate of Penetration using Artificial Intelligent Techniques

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

Application of machine learning models for real-time prediction of the formation lithology and tops from the drilling parameters

Predicting Rate of Penetration Using Artificial Intelligence Techniques