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

Barbosa, Luís Felipe Ferreira Motta; Nascimento, Andreas; Mathias, Mauro Hugo; Carvalho, João Andrade de

doi:10.1016/j.petrol.2019.106332

Cited by 139 publications

(46 citation statements)

References 88 publications

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“…This subject is shared between geothermal and oil and gas industries where drilling operations are remarkably similar. There are myriad studies where machine learning techniques have successfully addressed the mentioned issues and provided reliable solutions to optimize the drilling stage (Barbosa et al 2019;Hegde et al 2020;Gray 2017, 2018;Noshi and Schubert 2018). Recently, the Department of Energy has funded a project with the theme of application of deep machine learning to optimize drilling operations (specifically for geothermal wells) which was awarded to Oregon State University with collaboration with one more US university, one DOE National Laboratory, in addition to four geothermal and oil and gas companies from Iceland, US and Norway (DOE, 2019).…”

Section: Drilling Stagementioning

confidence: 99%

Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

et al. 2021

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Geothermal scientists have used bottom-hole temperature data from extensive oil and gas well datasets to generate heat flow and temperature-at-depth maps to locate potential geothermally active regions. Considering that there are some uncertainties and simplifying assumptions associated with the current state of physics-based models, in this study, the applicability of several machine learning models is evaluated for predicting temperature-at-depth and geothermal gradient parameters. Through our exploratory analysis, it is found that XGBoost and Random Forest result in the highest accuracy for subsurface temperature prediction. Furthermore, we apply our model to regions around the sites to provide 2D continuous temperature maps at three different depths using XGBoost model, which can be used to locate prospective geothermally active regions. We also validate the proposed XGBoost and DNN models using an extra dataset containing measured temperature data along the depth for 58 wells in the state of West Virginia. Accuracy measures show that machine learning models are highly comparable to the physics-based model and can even outperform the thermal conductivity model. Also, a geothermal gradient map is derived for the whole region by fitting linear regression to the XGBoost-predicted temperatures along the depth. Finally, through our analysis, the most favorable geological locations are suggested for potential future geothermal developments.

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Section: Drilling Stagementioning

confidence: 99%

Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

et al. 2021

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“…Generally, many AI applications in the petroleum industry have been developed [19,20]. There are five AI techniques, which are artificial neural networks (ANN), support vector machines (SVM), fuzzy inference systems, neuro-fuzzy, and ensemble models [21].…”

Section: Applications Of Artificial Intelligence In the Petroleum Indmentioning

confidence: 99%

Real-Time Prediction of Rheological Properties of Invert Emulsion Mud Using Adaptive Neuro-Fuzzy Inference System

Alsabaa

Gamal

Elkatatny

et al. 2020

Sensors

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Tracking the rheological properties of the drilling fluid is a key factor for the success of the drilling operation. The main objective of this paper is to relate the most frequent mud measurements (every 15 to 20 min) as mud weight (MWT) and Marsh funnel viscosity (MFV) to the less frequent mud rheological measurements (twice a day) as plastic viscosity (PV), yield point (YP), behavior index (n), and apparent viscosity (AV) for fully automating the process of retrieving rheological properties. The adaptive neuro-fuzzy inference system (ANFIS) was used to develop new models to determine the mud rheological properties using real field measurements of 741 data points. The data were collected from 99 different wells during drilling operations of 12 ¼ inches section. The ANFIS clustering technique was optimized by using training to a testing ratio of 80% to 20% as 591 data points for training and 150 points, cluster radius value of 0.1, and 200 epochs. The results of the prediction models showed a correlation coefficient (R) that exceeded 0.9 between the actual and predicted values with an average absolute percentage error (AAPE) below 5.7% for the training and testing data sets. ANFIS models will help to track in real-time the rheological properties for invert emulsion mud that allows better control for the drilling operation problems.

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“…ANN has many applications in the petroleum industry as summarized by Al-Bulushi et al [23]. ANN has been applied in different aspects of petroleum engineering such as production forecasting [24,25], PVT (Pressure, volume, temperature) parameter prediction [26], well integrity evaluation [27], drilling fluid properties [28][29][30], reservoir, rock mechanics [31][32][33][34][35], drilling optimization [36][37][38][39][40][41], and permeability determination from well logs [42].…”

Section: Artificial Neural Network and Its Application In Drilling Opmentioning

confidence: 99%

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

Elkatatny

Al-AbdulJabbar

Abdelgawad

2020

Sensors

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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.

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

Cited by 139 publications

References 88 publications

Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

Exploratory analysis of machine learning methods in predicting subsurface temperature and geothermal gradient of Northeastern United States

Real-Time Prediction of Rheological Properties of Invert Emulsion Mud Using Adaptive Neuro-Fuzzy Inference System

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

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