Development of hybrid computational data-intelligence model for flowing bottom-hole pressure of oil wells: New strategy for oil reservoir management and monitoring

Goliatt, Leonardo; Mohammad, Reem Sabah; Abba, S.I.; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1016/j.fuel.2023.128623

Cited by 12 publications

(4 citation statements)

References 65 publications

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“…For this unknown reason, the fluid density must be in the constant test. However, while we tend to monitor and evaluate the drilling fluid profiles in constant check, artificial and deep neural networks are by far employed to reduce time-cost [23][24][25] effects on the drilling and production of hydrocarbons in the petroleum and civil engineering industries. Its application to solve complex and technical problems is derived from biological neuron concepts.…”

Section: Rheologymentioning

confidence: 99%

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Filter Cake Neural-Objective Data Modelling and Image Optimization

Wayo,

Irawan,

Satyanaga

et al. 2024

Preprint

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Neural-objective and image optimization approaches for drilling fluid rheology automation are crucial for drilling engineering optimization. A myriad of intelligent computational models are employed to predict and monitor the parameters of mud rheology and filter cake permeability posture using an artificial neural network feedforward (ANN-FF) function, a non-ANN-FF function, an image processing tool, and a model optimization tool. 498 datasets of synthetic-based mud (SBM) flat rheology from a drilling mud laboratory at Nazarbayev University were imported into a user-friendly MATLAB application using image processing and nftool. Apart from the Google TensorFlow Sequential API DNN architecture used, a Levenberg-Marquardt training algorithm with input sigmoid hidden neurons 10, 12, and 18 coupled with a linear output layer was also used to predict the SBM flow index. OBM and SBM filter cakes were processed for void spaces; however, a model study was extrapolated to maximize filtration volume Vf. The study’s findings show that the ANN-FF model employed for rheological property monitoring and prediction had higher steady and exponential levels of accuracy and a correlation coefficient of 0.96-0.99. More so, SBM and OBM image processing presented an area of void spaces of 1790M2 and 1739M2, respectively. The porosity and permeability postures of both SBM and OBM resulted in a significant void space capable of maximizing the flow index. In addition, the single-objective modelling based on a genetic algorithm did validate the experimental rheological data for flow index or filtration volume Vf optimization; the study resulted in the finding that the constrained physics-informed objective function hindered maximizing oil recovery and instead predicted possible formation damage. It is empirical to note that automating flow predictions with neural, objective, and image functions has presented an alternative novel method for non-programmers using MATLAB and Google Colabs that is capable of enhancing mud rheological parameters, drilling efficiency, and hydrocarbon recovery.

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Section: Rheologymentioning

confidence: 99%

“…Its application to solve complex and technical problems is derived from biological neuron concepts. This has the potential to solve linear and non-linear problems [23,24] based on adaptable models and datasets. More so, optimizing the prediction of fluid rheology [26,27] requires an efficient computational model.…”

Section: Rheologymentioning

confidence: 99%

Filter Cake Neural-Objective Data Modelling and Image Optimization

Wayo,

Irawan,

Satyanaga

et al. 2024

Preprint

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“…Equation ( 13) is used to predict the void fraction of the bubble flow and calculate the physical properties of the gas-liquid mixed fluid, which is brought into Equation (8).…”

Section: Bubble Flowmentioning

confidence: 99%

“…These efforts have led to the development of distinctive two-phase pressure drop calculation models, including vertical pipe flow [1][2][3], inclined pipe flow [4], and horizontal pipe flow [5,6]. Subsequently, other researchers have built upon these foundational models, making improvements to extend their applicability to more complex scenarios [7][8][9]. For the gas-liquid two-phase model in the annular, many researchers have established physical experiments to simulate the gas-liquid flow of the annular and used the experimental data to study the cross-sectional distribution of the gas in the annular tube, thus obtaining the experimental relationship for predicting the void fraction, and, finally, they obtained the annular two-phase flow model.…”

Section: Introductionmentioning

confidence: 99%

Zero-Net Liquid Flow Simulation Experiment and Flow Law in Casing Annulus Gas-Venting Wells

Yu,

Du,

Cao

et al. 2024

Processes

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Under casing annulus gas venting, the annulus of the well is in a special state of zero-net liquid flow (ZNLF), leading to gas production without liquid at the wellhead, resulting in significant holdup issues. Therefore, conventional two-phase flow models cannot be used for calculation. To study the flow characteristics of ZNLF in the annulus of the well, this study established a visual experimental device with a total height of 5.4 m, an outer pipe inner diameter of 140 mm, and an inner pipe outer diameter of 72 mm. The flow characteristics of ZNLF were studied by controlling the casing pressure, initial liquid level, and bottom gas injection rate. The experimental results showed that the flow patterns of ZNLF are mainly bubbly flow and churn flow. Bubbly flow occurred at lower gas rates, while churn flow occurred at higher gas rates. In addition, the experiment found that when the gas injection rate and initial liquid column height were controlled to be the same, the liquid holdup decreased as the casing pressure increased. Analysis of the data patterns indicated that the slip velocity is related to the casing pressure. Based on the experimental results of ZNLF in the annulus, this study established standards for flow pattern transitions, holdup, and a pressure drop calculation model. The model results showed good agreement with the experimental results, with errors not exceeding ±5%.

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