Prediction of Bottom-Hole Pressure Differential During Tripping Operations Using Artificial Neural Networks (ANN)

Sadeq, Muhammad Jafar; Kabir, S. Rayhan; Haque, Rafita; Ferdaws, Jannatul; Akhtaruzzaman, Md.; Forhat, Rokeya; Allayear, Shaikh Muhammad

doi:10.1007/978-981-15-3284-9_41

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…Accurately predicting the equivalent circulating density (ECD) during tripping in/out and drilling operations is crucial in ensuring safe and cost-effective well drilling. There are several empirical and physics-based hydraulics models available in the literature [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. However, the application of the models is limited to the considered assumptions and model controlling parameters.…”

Section: Discussionmentioning

confidence: 99%

“…They have conducted several experiments and also developed various models based on assumptions such as steady-state and transient conditions, non-slip at the wall, different flow scenarios, fluid rheological properties, well configurations, and operational parameters. Amir et al (2022) [1,2] extensively reviewed existing swab and surge models, including contributions from Burkhardt (1961) [3], Schuh (1964) [4], Fontenot and Clark (1974) [5], Mitchell (1988) [6], Ahmed (2008) [7], Crespo (2010) [8], Srivastav (2012) [9], Gjerstad (2013) [10], [11], Fredy (2012) [12], Erge (2015) [13], [14], Evren M. (2018) [15], Ettehadi (2018) [16], Shwetank (2020) [17,18], Zakarya (2021) [19], and Amir et al (2023) [20]. However, these 2 of 19 models did not consider all the parameters that affect the swab and surge, and their applicability to estimate experimental data is limited to the specific assumptions and setup conditions.…”

Section: Introductionmentioning

confidence: 99%

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Field Telemetry Drilling Dataset Modeling with Multivariable Regression, Group Method Data Handling, Artificial Neural Network, and the Proposed Group-Method-Data-Handling-Featured Artificial Neural Network

Mohammad,

Belayneh

2024

Applied Sciences

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This paper presents data-driven modeling and a results analysis. Group method data handling (GMDH), multivariable regression (MVR), artificial neuron network (ANN), and new proposed GMDH-featured ANN machine learning algorithms were implemented to model a field telemetry equivalent mud circulating density (ECD) dataset based on surface and subsurface drilling parameters. Unlike the standard GMDH-ANN model, the proposed GMDH-featured ANN utilizes a fully connected network. Based on the considered eighteen experimental modeling designs, all the GMDH regression results showed higher R-squared and minimum mean-square error values than the multivariable regression results. In addition, out of the considered eight experimental designs, the GMDH-ANN model predicts about 37.5% of the experiments correctly, while both algorithms have shown similar results for the remaining experiments. However, further testing with diverse datasets is necessary for better evaluation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Field Telemetry Drilling Dataset Modeling with Multivariable Regression, Group Method Data Handling, Artificial Neural Network, and the Proposed Group-Method-Data-Handling-Featured Artificial Neural Network

Mohammad,

Belayneh

2024

Applied Sciences

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“…At the same time, IoT is a very essential network mechanism in the smart grid system [20]. Cloud of Things [21] is a cloud layer of IoT used for monitoring, managing, and analyzing data and information provided by using a cloud server [22] [23]. A deep learning-based CoT model has been implemented to identify the traffic in a heterogeneous network [24].…”

Section: Deep Learning Process Contains Artificialmentioning

confidence: 99%

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

et al. 2020

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Load forecasting is a vital part of smart grids for predicting the required electrical power using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the smart grid using the artificial neural network (ANN). Generally, computing the deep learning in the smart grid requires massive data aggregation or centralization and significant computational time. This paper presents a survey of deep learning-based load forecasting techniques from 2015 to 2020. This survey discusses the studies based on their deep learning techniques, Distributed Deep Learning (DDL) techniques, Back Propagation (BP) based works, and non-BP based works in the load forecasting process. Consequent to the survey, it was determined that data aggregation dependency would be beneficial for reducing computational time in load forecasting. Therefore, a conceptual model of DDL for smart grids has been presented, where the HSIC (Hilbert-Schmidt Independence Criterion) Bottleneck technique has been incorporated to provide higher accuracy.

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“…Ettehadi et al (2018) developed an analytical model for calculating pressure surges caused by drill string movement in Herschel-Bulkley fluids [14]. Shwetank et al (2020) developed a two-layer neural network to predict swab and surge pressures [15]. Shwetank et al (2020) also performed a parametric study to identify the impact of different parameters on the surge and swab pressures [16].…”

Section: Introductionmentioning

confidence: 99%

Physics-Based Swab and Surge Simulations and the Machine Learning Modeling of Field Telemetry Swab Datasets

Mohammad,

Belayneh,

Davidrajuh

2023

Applied Sciences

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Drilling operations are the major cost factor for the oil industry. Appropriately designed operations are essential for successful drilling. Optimized drilling operations also enhance drilling performance and reduce drilling costs. This is achieved by increasing the bit life (minimizing premature bit wear), drilling more quickly, which reduces drilling time, and also reducing tripping operations. This paper is presented in two parts. The first part compares the parametric physics-based swab and surge simulation results obtained from the Bingham plastic, power law, and Robertson–Stiff models. The aim is to show how the model’s predictions deviate from each other. Two 80:20 oil/water ratio (OWR) oil-based drilling fluids and two 90:10 OWR oil-based drilling fluids, 1.96 sg and 2.0 sg, were considered in vertical and deviated wells. Analysis of the simulation results revealed that the deviations depend on the drilling fluid’s physical and rheological parameters as well as the well trajectory. Moreover, the model’s predictions were inconsistent. Data-driven machine learning (ML) modeling is the focus of the second section. Data-driven modeling was performed using both software-generated datasets and field datasets. The results show that the random forest regressor (RF), artificial neural network (ANN), long short-term memory (LSTM), LightGBM, XGBoost, and multivariate regression models predicted the training and test datasets with higher R-squared and minimum mean square error values. Deploying the ML model in real-time applications and the planning phase would lead to potential applications of artificial intelligence for well planning and optimization processes.

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Prediction of Bottom-Hole Pressure Differential During Tripping Operations Using Artificial Neural Networks (ANN)

Cited by 5 publications

References 18 publications

Field Telemetry Drilling Dataset Modeling with Multivariable Regression, Group Method Data Handling, Artificial Neural Network, and the Proposed Group-Method-Data-Handling-Featured Artificial Neural Network

Field Telemetry Drilling Dataset Modeling with Multivariable Regression, Group Method Data Handling, Artificial Neural Network, and the Proposed Group-Method-Data-Handling-Featured Artificial Neural Network

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

Physics-Based Swab and Surge Simulations and the Machine Learning Modeling of Field Telemetry Swab Datasets

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