An Integrated Method to Characterize Shale Gas Reservoir Performance
Bibliographic record
Abstract
Abstract The application of horizontal well drilling coupled with the multistage fracturing technology enables commercial development of shale gas formations. However, due to the complexity of fracture network propagation, simulation of such reservoirs is challenging and associated with uncertainties. In order to minimize the uncertainty of modeling, we correlate first-hand pumping schedule data with the reservoir performance directly through coupling a fracking process with a reservoir simulator. This provides us an integrated way to characterize a well trajectory, hydraulic fracture configurations and shale gas reservoir performance. In addition, a geomechanical effect on the reservoir performance under certain fracture configurations is studied using a geomechanics module developed by CMG Ltd. GOHFER is widely used in a hydraulic fracking analysis. In this work, we couple GOHFER simulation output with the CMG module to determine the hydraulic fracture configuration. Thus, a method to correlate the first-hand pumping data (a slurry rate, slurry concentration and pumping pressure) with the reservoir simulator is given. Because of the stress sensitivity of a shale formation, we employ a linear-elastic constitutive law to depict the rock behavior with Young's modulus of 5,000,000 psi and Poisson's ratio of 0.2. Moreover, a Barton-Bandis model is used to describe the tensile opening of natural fractures for the dual-permeability reservoir model. From a series of numerical simulation studies, we find that the effective normal stress will increase with the development of a shale gas reservoir which will lead to a decrease in porosity and permeability. For the base case without a geomechanics effect, it will produce higher cumulative gas production than the case with the geomechanics effect. When producing for six months, the difference of the cumulative gas production between the two cases is 14.3%. The integrated process provides insights about shale gas reservoir performance with available data and handy tools.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".