Artificial intelligence techniques and their applications in drilling fluid engineering: A review

Agwu, Okorie E.; Akpabio, Julius U.; Alabi, Sunday Boladale; Dosunmu, Adewale

doi:10.1016/j.petrol.2018.04.019

Cited by 128 publications

(54 citation statements)

References 46 publications

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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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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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“…Artificial intelligence, (AI), can be simply defined as the computer science branch for creating intelligent machines [26] to exhibit human brains to make predictions and help take the right decisions for the future scenarios [27]. Recently, different AI methods such as fuzzy logic, FL, support vector machine, SVM, genetic algorithm and artificial neural network, ANN, have been applied in petroleum engineering, and specifically in the field of drilling fluid engineering.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…Based on the literature, more than 50 percent of the applications in the drilling fluid area used ANN for the predictions and got high accurate results. Accordingly, ANN has been selected for building the proposed models in this study [26].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…Figure 1 shows that MW has R of 0.36 and 0.76 with Y P and PV respectively while MF has R of 0.86 with Y P and 0.36 with PV. building the proposed models in this study [26].…”

Section: Data Descriptionmentioning

confidence: 99%

“…Eventually, the predicted values of n and k can be estimated using Equations (26) and (27), respectively. n = 0.244(n n + 1) + 0.263 (26) k = 2.78(k n + 1) + 0.731 (27)…”

Section: Input Layer Biasesmentioning

confidence: 99%

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Data-Driven Framework to Predict the Rheological Properties of CaCl2 Brine-Based Drill-in Fluid Using Artificial Neural Network

et al. 2019

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Calcium chloride brine-based drill-in fluid is commonly used within the reservoir section, as it is specially formulated to maximize drilling experience, and to protect the reservoir from being damaged. Monitoring the drilling fluid rheology including plastic viscosity, P V , apparent viscosity, A V , yield point, Y p , flow behavior index, n , and flow consistency index, k , has great importance in evaluating hole cleaning and optimizing drilling hydraulics. Therefore, it is very crucial for the mud rheology to be checked periodically during drilling, in order to control its persistent change. Such properties are often measured in the field twice a day, and in practice, this takes a long time (2–3 h for taking measurements and cleaning the instruments). However, mud weight, M W , and Marsh funnel viscosity, M F , are periodically measured every 15–20 min. The objective of this study is to develop new models using artificial neural network, ANN, to predict the rheological properties of calcium chloride brine-based mud using M W and M F measurements then extract empirical correlations in a white-box mode to predict these properties based on M W and M F . Field measurements, 515 points, representing actual mud samples, were collected to build the proposed ANN models. The optimized parameters of these models resulted in highly accurate results indicated by a high correlation coefficient, R, between the predicted and measured values, which exceeded 0.97, with an average absolute percentage error, AAPE, that did not exceed 6.1%. Accordingly, the developed models are very useful for monitoring the mud rheology to optimize the drilling operation and avoid many problems such as hole cleaning issues, pipe sticking and loss of circulation.

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Artificial Intelligence (AI) for the Future of the Oil and Gas (O&G) Industry

2023

The Power of Artificial Intelligence for the Next‐Generation Oil and Gas Industry

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Artificial intelligence techniques and their applications in drilling fluid engineering: A review

Cited by 128 publications

References 46 publications

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

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

Data-Driven Framework to Predict the Rheological Properties of CaCl2 Brine-Based Drill-in Fluid Using Artificial Neural Network

Artificial Intelligence (AI) for the Future of the Oil and Gas (O&G) Industry

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