Factors affecting drilling incidents: Prediction of suck pipe by XGBoost model

Kizayev, Talgat; Irawan, Sonny; Khan, Javed Akbar; Khan, Sumeer Ahmad; Cai, Baoping; Zeb, Nazia; Wayo, Dennis Delali Kwesi

doi:10.1016/j.egyr.2023.03.083

Cited by 5 publications

(2 citation statements)

References 29 publications

(29 reference statements)

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“…An efficient circulation of mud in the wellbore, maintaining a constant hydrostatic pressure in the drilling column, safe carriage of cuttings, effective reduction of drilling bit and string temperatures, and good hole cleaning are all reliant on a good drilling mud formulation [1,2]. This in its entirety prevents fluid pressures from drilled formations invading the wellbore, and if this is not properly managed, a devastating blowout is expected.…”

Section: Research Backgroundmentioning

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: Research Backgroundmentioning

confidence: 99%

“…It is worth noting that the study considered converting the actual pixels of the image to micrometres. The RGB-SBM image pixel area was 25570, and the conversion to an area micrometre yielded 1790M 2 .…”

Section: Cake Image Optimizationmentioning

confidence: 99%