A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning

Mohamadian, Nima; Ghorbani, Hamzeh; Wood, David A.; Mehrad, Mohammad; Davoodi, Shadfar; Rashidi, Sina; Soleimanian, Alireza; Shahvand, Amirafzal Kiani

doi:10.1016/j.petrol.2020.107811

Cited by 70 publications

(29 citation statements)

References 29 publications

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“…Because of the variety of complexities for each dependent variable, there are many different types of neural networks. The selection of attributes (i.e., the input variables to be considered), network architecture (number of layers and nodes), and the transfer of functions between layers, as well as the selection of training algorithm, are among the factors that increase the correct choice of these factors and thus increase the performance accuracy in artificial networks (Ghorbani et al 2018;Mohamadian et al 2021). The multi-layer perceptron (Fig.…”

Section: Multi-layer Perceptronmentioning

confidence: 99%

Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

Rajabi

Beheshtian

Davoodi

et al. 2021

J Petrol Explor Prod Technol

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One of the challenges in reservoir management is determining the fracture density (FVDC) in reservoir rock. Given the high cost of coring operations and image logs, the ability to predict FVDC from various petrophysical input variables using a supervised learning basis calibrated to the standard well is extremely useful. In this study, a novel machine learning approach is developed to predict FVDC from 12-input variable well-log based on feature selection. To predict the FVDC, combination of two networks of multiple extreme learning machines (MELM) and multi-layer perceptron (MLP) hybrid algorithm with a combination of genetic algorithm (GA) and particle swarm optimizer (PSO) has been used. We use a novel MELM-PSO/GA combination that has never been used before, and the best comparison result between MELM-PSO-related models with performance test data is RMSE = 0.0047 1/m; R2 = 0.9931. According to the performance accuracy analysis, the models are MLP-PSO < MLP-GA < MELM-GA < MELM-PSO. This method can be used in other fields, but it must be recalibrated with at least one well. Furthermore, the developed method provides insights for the use of machine learning to reduce errors and avoid data overfitting in order to create the best possible prediction performance for FVDC prediction.

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Section: Multi-layer Perceptronmentioning

confidence: 99%

Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

Rajabi

Beheshtian

Davoodi

et al. 2021

J Petrol Explor Prod Technol

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“…The oil and gas industry, which has long been the focus of the whole world, and the reason is that the extracted oil and gas has changed the world. Many people did a lot of work to optimize and find important and key parameters in the oil and gas industry, for example in the following areas have been addressed: prediction flow rate of orifice & choke flow [22-23; 39-40, 44]; prediction of casing collapse based on shear modulus & geomechanical approach [41,43]; prediction of bubble point pressure [42].…”

Section: Machine Learning Algorithmmentioning

confidence: 99%

Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm

Hazbeh

Alvar

Aghdam

et al. 2021

Journal of Petroleum and Mining Engineering

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Achieving important and effective reservoir parameters requires a lot of time and cost, and also achieving these devices is sometimes not possible. In this research, a dataset including 565 datapoints collected from published articles have been used. The input data for forecasting oil formation volume factor (OFVF) were solution gas oil ratio (Rs), gas specific gravity (γg), API gravity (API0) (or oil density γo), and temperature (T). Two hybrid methods multilayer perceptron (MLP) with artificial bee colony (ABC) and firefly (FF) algorithms to predict this parameter have been introduced in that study and their results have been compered after extraction. After essential investigations in this study, the results show that MLP-ABC gives the best accuracy for predicting OFVF. For MLP-ABC model OFVF prediction accuracy in terms of RMSE < 0.002573 bbl/STB and R2 = 0.998 for this test dataset. After comparing the results of the experimental equations, it was concluded that the Dokla and Osman model gives the best results and Based on Spearman's correlation coefficient relationships all input parameters have a positive effect on OFVF prediction, which are as follows: Rs> T> API> γg and these results show that the effect of Rs is more than other input variables and the effect of γg is the lowest.

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“…The oil and gas industry, which has long been the focus of the whole world, and the reason is that the extracted oil and gas has changed the world. Many people did a lot of work to optimize and find important and key parameters in the oil and gas industry [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58]. The sustainability of wellbore, materials and drilling equipment's is severely threatened by well blowouts.…”

Section: Introductionmentioning

confidence: 99%

Petroleum Well Blowouts as a Threat to Drilling Operation and Wellbore Sustainability: Causes, Prevention, Safety and Emergency Response

Ghorbani¹,

Researchers²,

Abdali³

et al. 2021

JCM

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Sustainability in petroleum wells drilling operation systems strongly depends on the use of sustainable materials and a set of technical and safety measures that lead to the survival and proper operation of drilling rig equipment's and personnel. Adherence to the highest levels of standards of tools, materials and methods, although always recommended as the most important option for advancing a safe drilling operation and completing the well efficiently, low risk and stable, but drilling operation is inherently a battle with underground challenges and unexpected dangers. Learning from past such well blowout events and the problems they pose to rapidly control is essential to reduce future impacts including injuries, damage and emissions. Such analysis offers guidance for adapting working practices to improve both prevention and emergency response to such incidents. The causes of blowout during drilling and the necessary technical and safety measures to adopt are reviewed, highlighting how best practices can prevent blowout incidents by improving responses to early warning signals. The particular risks associated with potential shallow gas blowouts are identified and described with the aid of a case study associated with a catastrophic blowout of an onshore well in Iran and the methods used to ultimately control it. The multiple causes of the incident relating to defects in safety systems, equipment and operating procedures are addressed. Lessons learned from the incident reveal the complexity of well control once a blowout incident has occurred and developed into a surface fire. from the stage of the incident to fire control. There is a need for further research into top-hole well kill techniques for wells in a blowout state, as drilling bottom-hole relief wells takes substantial time, during which much surface damage, resource loss and emission typically occurs.

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A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning

Cited by 70 publications

References 29 publications

Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm

Petroleum Well Blowouts as a Threat to Drilling Operation and Wellbore Sustainability: Causes, Prevention, Safety and Emergency Response

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