1D and 3D Geomechanical Modeling of an Unconventional Shale Gas Formation for Well Placement Location and Completions Optimization
Bibliographic record
Abstract
ABSTRACT: In unconventional shale gas formations, hydraulic fracturing is essential for production, making well placement and strategic planning crucial for maximising production output. PETRONAS employs machine learning (ML) techniques, particularly artificial neural network (ANN), to generate sweet spot maps located within Montney Shale Gas Formation in Canada for optimal well placement and field planning. Key inputs for the ML algorithm come from the 3D geomechanical model developed for Field A. This process begins with six (6) 1D geomechanical models, calibrated using pressure measurement data, rock mechanical properties, and drilling experience. These 1D models serve as inputs for the 3D geomechanical model, where the rock mechanical properties generated are propagated using co-kriging method. Rock strength properties for the 3D model are determined through specialised correlations developed for the field whilst 3D bulk density volume is used to calculate overburden stress, 3D compressional velocity volume is used for pore pressure prediction using Bower's method and finally, horizontal stress gradient method is applied for horizontal stresses determination. The 3D stresses are validated against the 1D geomechanical models for accuracy. The extracted 3D geomechanical data are then used in ANN algorithms to develop sweet spot maps, which guide well placement and field planning and maximizing production and economic viability 1. INTRODUCTION Unconventional reservoirs, such as shale gas formations, present considerable production challenges due to their low-permeability source rocks that retain hydrocarbons in situ, rather than allowing migration to reservoir rocks. In contrast to conventional reservoirs, hydrocarbons in unconventional reservoirs require stimulation techniques, such as hydraulic fracturing, to enable extraction. Hydraulic fracturing, entails the injection of high-pressure fluids to create fractures in the reservoir rock, significantly enhancing permeability and facilitating the flow of hydrocarbons towards production wells which is a critical process for the economic recovery of shale gas (Aminzadeh, 2020 and Shi et al., 2022). The Montney Shale Gas Formation, located in British Columbia and Alberta, Canada, serves as a notable example of such an unconventional reservoir. This extensive formation is a major source of natural gas and natural gas liquids, playing a vital role in Canada's energy supply. Since 2013, PETRONAS Canada has been actively engaged in the North Montney Joint Venture (NMJV), focusing on the exploration and production of gas from this formation. The extraction strategy involves the drilling of horizontal wells, followed by hydraulic fracturing to stimulate gas production. The precise geosteering and placement of these horizontal wells are crucial for maximizing reservoir contact, optimizing recovery factors, and ensuring the economic feasibility of field development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".