scholarly journals New Artificial Neural Networks Model for Predicting Rate of Penetration in Deep Shale Formation

2019 ◽  
Vol 11 (22) ◽  
pp. 6527 ◽  
Author(s):  
Ahmed ◽  
Ali ◽  
Elkatatny ◽  
Abdulraheem
Keyword(s):  
Artificial Intelligence ◽  
Petroleum Industry ◽  
Real Field ◽  
Percentage Error ◽  
Individual Parameter ◽  
Rate Of Penetration ◽  
Drilling Parameters ◽  
Drilling Operations ◽  
Shale Formation ◽  
Artificial Neural

Rate of penetration (ROP) means how fast the drilling bit is drilling through the formations. It is known that in the petroleum industry, most of the well cost is taken by the drilling operations. Therefore, it is very crucial to drill carefully and improve drilling processes. Nevertheless, it is challenging to predict the influence of every single parameter because most of the drilling parameters depend on each other and altering an individual parameter will have an impact on the rest. Due to the complexity of the drilling operations, up to the present time, there is no reliable model that can adequately estimate the ROP. Artificial intelligence (AI) might be capable of building a predictive model from a number of input parameters that correlate to the output parameter. A real field dataset, of shale formation, that contains records of both drilling parameters such as, rotation per minute (RPM), weight on bit (WOB), drilling torque (τ), standpipe pressure (SPP) and flow pump (Q) and mud properties such as, mud weight (MW), funnel and plastic viscosities (FV) (PV), solid (%) and yield point (YP) were used to predict ROP using artificial neural network (ANN). A comparison between the developed ANN-ROP model and the number of selected published ROP models were performed. A novel empirical equation of ROP using the above-mentioned parameters was derived based on ANN technique which is able to estimate ROP with excellent precision (correlation coefficient (R) of 0.996 and average absolute percentage error (AAPE) of 5.776%). The novel ANN-based correlation outperformed three published empirical models and it can be used to predict the ROP without the need for artificial intelligence software.

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

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2058 ◽  
Author(s):  
Salaheldin Elkatatny ◽  
Ahmed Al-AbdulJabbar ◽  
Khaled Abdelgawad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mutual Effect ◽  
Percentage Error ◽  
Drilling Process ◽  
Ann Model ◽  
Data Set ◽  
Individual Parameter ◽  
Rate Of Penetration ◽  
Artificial Neural

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.

scholarly journals Prediction of Rate of Penetration for wells at Nam Con Son basin using Artificial Neural Networks models

10.29007/4sdt ◽  
2022 ◽  
Author(s):  
Vu Khanh Phat Ong ◽  
Quang Khanh Do ◽  
Thang Nguyen ◽  
Hoang Long Vo ◽  
Ngoc Anh Thy Nguyen ◽  
...  
Keyword(s):  
Network Model ◽  
Oil And Gas ◽  
Research Approach ◽  
Ann Model ◽  
Rate Of Penetration ◽  
Drilling Parameters ◽  
Drilling Operations ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The rate of penetration (ROP) is an important parameter that affects the success of a drilling operation. In this paper, the research approach is based on different artificial neural network (ANN) models to predict ROP for oil and gas wells in Nam Con Son basin. The first is the process of collecting and evaluating drilling parameters as input data of the model. Next is to find the network model capable of predicting ROP most accurately. After that, the study will evaluate the number of input parameters of the network model. The ROP prediction results obtained from different ANN models are also compared with traditional models such as the Bingham model, Bourgoyne & Young model. These results have shown the competitiveness of the ANN model and its high applicability to actual drilling operations.

Fracture Pressure Prediction Using Surface Drilling Parameters by Artificial Intelligence Techniques

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Abdulmalek Ahmed ◽  
Salaheldin Elkatatny ◽  
Abdulwahab Ali
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Optimal Model ◽  
Time Data ◽  
Fracture Pressure ◽  
Drilling Parameters ◽  
Drilling Operations

Abstract Several correlations are available to determine the fracture pressure, a vital property of a well, which is essential in the design of the drilling operations and preventing problems. Some of these correlations are based on the rock and formation characteristics, and others are based on log data. In this study, five artificial intelligence (AI) techniques predicting fracture pressure were developed and compared with the existing empirical correlations to select the optimal model. Real-time data of surface drilling parameters from one well were obtained using real-time drilling sensors. The five employed methods of AI are functional networks (FN), artificial neural networks (ANN), support vector machine (SVM), radial basis function (RBF), and fuzzy logic (FL). More than 3990 datasets were used to build the five AI models by dividing the data into training and testing sets. A comparison between the results of the five AI techniques and the empirical fracture correlations, such as the Eaton model, Matthews and Kelly model, and Pennebaker model, was also performed. The results reveal that AI techniques outperform the three fracture pressure correlations based on their high accuracy, represented by the low average absolute percentage error (AAPE) and a high coefficient of determination (R2). Compared with empirical models, the AI techniques have the advantage of requiring less data, only surface drilling parameters, which can be conveniently obtained from any well. Additionally, a new fracture pressure correlation was developed based on ANN, which predicts the fracture pressure with high precision (R2 = 0.99 and AAPE = 0.094%).

Drilling Rate of Penetration Prediction of High-Angled Wells Using Artificial Neural Networks

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Ahmed K. Abbas ◽  
Salih Rushdi ◽  
Mortadha Alsaba ◽  
Mohammed F. Al Dushaishi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Oil And Gas ◽  
Complex Problem ◽  
Real Field ◽  
Operational Parameters ◽  
Rate Of Penetration ◽  
Weight On Bit ◽  
Artificial Neural ◽  
Deviated Wells

Predicting the rate of penetration (ROP) is a significant factor in drilling optimization and minimizing expensive drilling costs. However, due to the geological uncertainty and many uncontrolled operational parameters influencing the ROP, its prediction is still a complex problem for the oil and gas industries. In the present study, a reliable computational approach for the prediction of ROP is proposed. First, fscaret package in a R environment was implemented to find out the importance and ranking of the inputs’ parameters. According to the feature ranking process, out of the 25 variables studied, 19 variables had the highest impact on ROP based on their ranges within this dataset. Second, a new model that is able to predict the ROP using real field data, which is based on artificial neural networks (ANNs), was developed. In order to gain a deeper understanding of the relationships between input parameters and ROP, this model was used to check the effect of the weight on bit (WOB), rotation per minute (rpm), and flow rate (FR). Finally, the simulation results of three deviated wells showed an acceptable representation of the physical process, with reasonable predicted ROP values. The main contribution of this research as compared to previous studies is that it investigates the influence of well trajectory (azimuth and inclination) and mechanical earth modeling parameters on the ROP for high-angled wells. The major advantage of the present study is optimizing the drilling parameters, predicting the proper penetration rate, estimating the drilling time of the deviated wells, and eventually reducing the drilling cost for future wells.

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

2020 ◽  
Vol 12 (4) ◽  
pp. 1376 ◽  
Author(s):  
Ahmad Al-AbdulJabbar ◽  
Salaheldin Elkatatny ◽  
Ahmed Abdulhamid Mahmoud ◽  
Tamer Moussa ◽  
Dhafer Al-Shehri ◽  
...  
Keyword(s):  
Empirical Correlation ◽  
The Self ◽  
Carbonate Reservoirs ◽  
Ann Model ◽  
Horizontal Drilling ◽  
Rate Of Penetration ◽  
Drilling Parameters ◽  
Unseen Data ◽  
Artificial Neural ◽  
Self Adaptive

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.

A Robust Rate of Penetration Model for Carbonate Formation

2018 ◽  
Vol 141 (4) ◽  
Author(s):  
Ahmad Al-AbdulJabbar ◽  
Salaheldin Elkatatny ◽  
Mohamed Mahmoud ◽  
Khaled Abdelgawad ◽  
Abdulaziz Al-Majed
Keyword(s):  
Compressive Strength ◽  
Drilling Fluid ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Drilling Process ◽  
Rate Of Penetration ◽  
New Model ◽  
Carbonate Formation ◽  
Drilling Parameters ◽  
Fluid Properties

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.

Artificial Intelligence-based Predictive Technique to Estimate Oil Formation Volume Factor

2021 ◽  
Author(s):  
Shams Kalam ◽  
Mohammad Rasheed Khan ◽  
Rizwan Ahmed Khan
Keyword(s):  
Artificial Intelligence ◽  
Crude Oil ◽  
Mean Squared Error ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Volume Factor ◽  
Oil Formation Volume Factor ◽  
Artificial Neural ◽  
Error Metric ◽  
Oil Formation

Abstract This investigation presents a powerful predictive model to determine crude oil formation volume factor (FVF) using state-of-the-art artificial intelligence (AI) techniques. FVF is a vital pressure-volume-temperature (PVT) parameter used to characterize hydrocarbon systems and is pivotal to reserves calculation and reservoir engineering studies. Ideally, FVF is measured at the laboratory scale; however, prognostic tools to evaluate this parameter can optimize time and cost estimates. The database utilized in this study is obtained from open literature and covers statistics of crude oils of the Middle East region. Multiple AI algorithms are considered, including Artificial Neural Networks (ANN) and Artificial Neural Fuzzy Inference Systems (ANFIS). Models are developed utilizing an optimization strategy for various parameters/hyper-parameters of the respective algorithms. Unique permutations and combinations for the number of perceptron and their resident layers is investigated to reach a solution that provides the most optimum output. These intelligent models are produced as a function of the parameters intrinsically affecting FVF; reservoir temperature, solution GOR, gas specific gravity, bubble point pressure, and crude oil API gravity. Comparative analysis of developed AI models is performed using visualization/statistical analysis, and the best model is pointed out. Finally, the mathematical equation extraction to determine FVF is accomplished with the respective weights and bias for the model presented. Graphical analysis is used to evaluate the performance of developed AI models. The results of scatter plots showed most of the points are lying on the 45 degree line. Moreover, during this study, an error metric is developed comprising of multiple analysis parameters; Average absolute percentage error (AAPE), Root Mean Squared Error (RMSE), coefficient of determination (R2). All models investigated are tested on an unseen dataset to prevent a biased model's development. Performance of the established AI models is gauged based on this error metric, demonstrating that ANN outperforms ANFIS with error within 1% of the measured PVT values. A computationally derived intelligent model provides the strongest predictive capabilities as it maps complex non-linear interactions between various input parameters leading to FVF.

Comparative Evaluation of Artificial Intelligence Models for Drilling Rate of Penetration Prediction

2021 ◽  
Author(s):  
Ololade Adetifa ◽  
Ibiye Iyalla ◽  
Kingsley Amadi
Keyword(s):  
Artificial Intelligence ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Artificial Intelligent ◽  
Rate Of Penetration ◽  
Drilling Data ◽  
Intelligent Models ◽  
Prediction Capability

Abstract Rate of penetration is an important parameter in drilling performance analysis. The accurate prediction of rate of penetration during well planning leads to a reduction in capital and operating costs which is vital given the recent downturn in oil prices. The industry is seen to embrace the use of novel technologies and artificial intelligence in its bid to be sustainable which is why this study focuses on the use of artificial intelligent models in predicting the rate of penetration. The predictive performance of three data-driven models [artificial neural network (ANN), extreme learning machine (ELM) and least-square support vector machine (LS-SVM)] were evaluated using actual drilling data based on three performance evaluation criteria [mean square error (MSE), coefficient of determination (R2) and average absolute percentage error (AAPE)]. The models were validated using the physics based Bourgoyne and Young's model. The results show that all three models performed to an acceptable level of accuracy based on the range of the actual drilling data because, although the ELM had the least MSE (1317.44) and the highest R2 (0.52 i.e. 52% prediction capability) the LS-SVM model had a smaller spread of predicted ROP when compared with the actual ROP and the ANN had the least AAPE (38.14). The results can be improved upon by optimizing the controllable predictors. Validation of the model's performance with the Bourgoyne and Young's model resulted in R2 of 0.29 or 29% prediction capability confirming that artificial intelligent models outperformed the physics-based model.

scholarly journals A Self-Adaptive Artificial Intelligence Technique to Predict Oil Pressure Volume Temperature Properties

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3490 ◽  
Author(s):  
Salaheldin Elkatatny ◽  
Tamer Moussa ◽  
Abdulazeez Abdulraheem ◽  
Mohamed Mahmoud
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Differential Evolution Algorithm ◽  
Empirical Correlation ◽  
Percentage Error ◽  
Empirical Correlations ◽  
Input Parameters ◽  
Artificial Neural ◽  
Self Adaptive

Reservoir fluid properties such as bubble point pressure (Pb) and gas solubility (Rs) play a vital role in reservoir management and reservoir simulation. In addition, they affect the design of the production system. Pb and Rs can be obtained from laboratory experiments by taking a sample at the wellhead or from the reservoir under downhole conditions. However, this process is time-consuming and very costly. To overcome these challenges, empirical correlations and artificial intelligence (AI) models can be applied to obtain these properties. The objective of this paper is to introduce new empirical correlations to estimate Pb and Rs based on three input parameters—reservoir temperature and oil and gas gravities. 760 data points were collected from different sources to build new AI models for Pb and Rs. The new empirical correlations were developed by integrating artificial neural network (ANN) with a modified self-adaptive differential evolution algorithm to introduce a hybrid self-adaptive artificial neural network (SaDE-ANN) model. The results obtained confirmed the accuracy of the developed SaDE-ANN models to predict the Pb and Rs of crude oils. This is the first technique that can be used to predict Rs and Pb based on three input parameters only. The developed empirical correlation for Pb predicts the Pb with a correlation coefficient (CC) of 0.99 and an average absolute percentage error (AAPE) of 6%. The same results were obtained for Rs, where the new empirical correlation predicts the Rs with a coefficient of determination (R2) of 0.99 and an AAPE of less than 6%. The developed technique will help reservoir and production engineers to better understand and manage reservoirs. No additional or special software is required to run the developed technique.

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