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

Ahmed, Abdulmalek; Ali, Abdulwahab; Elkatatny, Salaheldin; Abdulraheem, Abdulazeez

doi:10.3390/su11226527

Cited by 45 publications

(15 citation statements)

References 24 publications

(22 reference statements)

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“…ANN is the most used AI technique among all AI methods because an empirical correlation can be extracted from the optimized ANN model. Hence, numerous ANN models were developed for real-time applications, such as estimating the RPO and the drilling performance (Ahmed et al 2019;Arabjamaloei and Shadizadeh 2011). Gidh et al (2012) improved the drilling performance by predicting and managing the bit wear utilizing an artificial neural network technique.…”

Section: Drilling Performance Predictionmentioning

confidence: 99%

“…Finally, they recommended that downhole parameters such as nozzle size, drill bit wear, and rock strength should be considered inputs for predicting the ROP profiles. Ahmed et al (2019) presented a comparative study of predicting ROP using several intelligence techniques. ROP was predicted for two wells using an extreme learning machine, ANN, and SVR techniques.…”

Section: Drilling Performance Predictionmentioning

confidence: 99%

See 1 more Smart Citation

A systematic review of data science and machine learning applications to the oil and gas industry

Tariq

Aljawad

Hasan

et al. 2021

J Petrol Explor Prod Technol

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This study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventional reservoirs. The future of data science and ML in the oil and gas industry, highlighting what is required from ML for better prediction, is also discussed. This study also provides a comprehensive comparison of different ML techniques used in the oil and gas industry. With the arrival of powerful computers, advanced ML algorithms, and extensive data generation from different industry tools, we see a bright future in developing solutions to the complex problems in the oil and gas industry that were previously beyond the grip of analytical solutions or numerical simulation. ML tools can incorporate every detail in the log data and every information connected to the target data. Despite their limitations, they are not constrained by limiting assumptions of analytical solutions or by particular data and/or power processing requirements of numerical simulators. This detailed and comprehensive study can serve as an exclusive reference for ML applications in the industry. Based on the review conducted, it was found that ML techniques offer a great potential in solving problems in almost all areas of the oil and gas industry involving prediction, classification, and clustering. With the generation of huge data in everyday oil and gas industry activates, machine learning and big data handling techniques are becoming a necessity toward a more efficient industry.

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Section: Drilling Performance Predictionmentioning

confidence: 99%

Section: Drilling Performance Predictionmentioning

confidence: 99%

A systematic review of data science and machine learning applications to the oil and gas industry

Tariq

Aljawad

Hasan

et al. 2021

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

show abstract

“…In the oil and gas industry, many studies utilized ML techniques for finding solutions for practical challenges. − Intelligent models were accomplished by AI tools for many purposes such as identifying the formation lithology, predicting the formation and fracture pressures, estimating the properties of reservoir fluids, estimating the oil recovery factor, , predicting the tops of the drilled formation, rate of penetration (ROP) prediction and optimization for different drilled formations and well profiles, − determining the content of total organic carbon, − and estimating the rock static Young’s modulus, − predicting the compressional and shear sonic times, determining the rock failure parameters, detecting the downhole abnormalities during horizontal drilling, determining the wear of a drill bit from the drilling parameters, and predicting the rheological properties of drilling fluids in real time. ,− …”

Section: Predicting Ecd By Employing ML Techniquesmentioning

confidence: 99%

Machine Learning Models for Equivalent Circulating Density Prediction from Drilling Data

2021

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Equivalent circulating density (ECD) is considered a critical parameter during the drilling operation, as it could lead to severe problems related to the well control such as fracturing the drilled formation and circulation loss. The conventional way to determine the ECD is either by carrying out the downhole tool measurements or by using mathematical models. The downhole measurement is costly and has some limitations with the practical operations, while the mathematical models do not provide a high level of accuracy. Determination of the ECD should have a high level of accuracy, and therefore, the objective of this study is to employ machine learning techniques such as artificial neural networks (ANNs) and adaptive network-based fuzzy inference systems (ANFISs) to predict the ECD from only the drilling data with a high accuracy level. The study utilized drilling data from a horizontal drilling section that includes drilling parameters (penetration rate, rotating speed, torque, weight on bit, pumping rate, and pressure of standpipe). The models were built and tested from a data set that has 3570 data points, and another data set of 1130 measurements was employed for validating the models. The accuracy of the models was determined by key performance indices, which are the coefficient of correlation (R) and the average absolute percentage error (AAPE). The results showed the strong prediction capability for ECD from the two models through training, testing, and validation processes with R greater than 0.98 and a very low error of 0.3% for the ANN model, while ANFIS recorded R of 0.96 and AAPE of 0.7, and hence, the two models showed great performance for ECD estimation application. Also, the study introduces a newly developed equation for ECD determination from drilling data in real time.

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“…According to Ahmed et al [19], an artificial neural network is made up of many components such as neurons, hidden layers, transfer function, learning function, training function, and epoch size. Neurons are components that have specific input/output, and they are connected to form a network of nodes that makes the neural networks [20].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors

et al. 2020

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

Cited by 45 publications

References 24 publications

A systematic review of data science and machine learning applications to the oil and gas industry

A systematic review of data science and machine learning applications to the oil and gas industry

Machine Learning Models for Equivalent Circulating Density Prediction from Drilling Data

Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors

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