Loading...

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

Mohamadian, N ; Sharif University of Technology | 2021

726 Viewed
  1. Type of Document: Article
  2. DOI: 10.1016/j.petrol.2020.107811
  3. Publisher: Elsevier B.V , 2021
  4. Abstract:
  5. The casing-collapse hazard is one that drilling engineers seek to mitigate with careful well design and operating procedures. However, certain rock formations and their fluid pressure and stress conditions are more prone to casing-collapse risks than others. The Gachsaran Formation in south west Iran, is one such formation, central to oil and gas resource exploration and development in the Zagros region and consisting of complex alternations of anhydrite, marl and salt. The casing-collapse incidents in this formation have resulted over decades in substantial lost production and remedial costs to mitigate the issues surrounding wells with failed casing string. High and vertically-varying horizontal stresses across the Gachsaran formation are the challenge to predict and overcome. This study investigates casing collapse in wellbores from an established petroleum geomechanics perspective to develop and compare two hybrid neural-network models, multilayer perceptron's tuned, respectively, with a genetic algorithm (MLP-GA) and a particle swarm algorithm (MLP-PSO). These machine-learning algorithms are configured to predict Poisson's ratio (ϑ) and maximum horizontal stress (σH) from available well-log input data. A large dataset from three wellbores drilled though the Gachsaran Formation in the Marun oil field, the second largest in Iran, is used to construct and validate the hybrid MLP models. In the first step, 22323 data records from a collapsed wellbore are divided into training (15626 records; 70%) and independent testing subsets (6697 records; 30%). In the next step, in order to test whether the suggested algorithms could be generalized, the data sets of the other two wells in the field were tested. Those tests confirmed that the algorithms achieved high prediction accuracy for Poisson's ratio and maximum horizontal stress in other wellbores. © 2020 Elsevier B.V
  6. Keywords:
  7. Boreholes ; Energy resources ; Fertilizers ; Forecasting ; Genetic algorithms ; Geomechanics ; Horizontal wells ; Infill drilling ; Large dataset ; Machine learning ; Multilayer neural networks ; Oil field equipment ; Oil well logging ; Oil wells ; Particle swarm optimization (PSO) ; Petroleum prospecting ; Poisson ratio ; Predictive analytics ; Rock mechanics ; Well logging ; Well testing ; Collapse hazards ; Collapse predictions ; Exploration and development ; Horizontal stress ; Hybrid neural networks ; Petroleum geomechanics ; Prediction accuracy ; Stress condition ; Learning algorithms ; Artificial neural network ; Gas supply ; Gas well ; Genetic algorithm ; Hydrocarbon exploration ; Oil supply ; Oil well ; Prediction ; Iran ; Zagros
  8. Source: Journal of Petroleum Science and Engineering ; Volume 196 , 2021 ; 09204105 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S092041052030872X