Stimulation Effectiveness in Multilayered, Tight Gas Reservoirs: Pinedale Anticline Area
Bibliographic record
Abstract
This article, written by Assistant Technology Editor Karen Bybee, contains highlights of paper SPE 100574, "Using Reservoir Modeling To Evaluate Stimulation Effectiveness in Multilayered ‘Tight’ Gas Reservoirs: A Case History in The Pinedale Anticline Area," by S.K. Schubarth, SPE, Schubarth Inc.; J.P. Spivey, SPE, Phoenix Reservoir Software LLC; and P.T. Huckabee, SPE, Shell E&P Co., prepared for the 2006 SPE Gas Technology Symposium, Calgary, 15–17 May. Low-permeability, or "tight," gas reservoirs are being developed at an ever-increasing rate in the U.S. Currently, there are several tight gas plays in the U.S. where multiple intervals are commingled for economic viability. The Pinedale anticline completions pose a complex problem in determining the best stimulation method because as many as 22 separate stimulation treatments are placed in as many as 70 discrete sands over a 6,000-ft-thick interval. Evaluation is complicated by permeability variations that exceed two orders of magnitude and pore-pressure gradients that range from 0.22 to 0.83 psi/ft. Introduction Development of the Pinedale anticline in southwestern Wyoming has continued at an aggressive pace over the past several years. Massive hydraulic-fracturing (MHF) treatments are the only way to stimulate production from the tight gas sands present in this area to economically acceptable levels. Generally, each well requires between 14 and 22 MHF treatments to produce effectively from all potential pay intervals. Often, more than 2 million lbm of proppant is used per well, representing a high investment cost to the operator. The ability to evaluate the incremental production associated with the use of one proppant vs. another can have a significant effect on field profitability. Data Analysis Production and completion information from 11 wells with 172 fracture-treated intervals was included in this analysis. Production information was updated, and all 11 wells have at least two production logs, with one early in the well life. A detailed production history also was available for all wells for use in the reservoir-simulation evaluation. Six different proppants were used in the fracture treatments pumped. Reservoir Simulation. For single-layer tight gas reservoirs, fracture and reservoir properties usually are estimated by analyzing production data. Methods for such single-layer analysis include advanced decline-curve analysis with constant-terminal-pressure type curves, type-curve matching with constant-terminal-rate type curves, and automatic history matching with a single-layer analytical reservoir simulator. Single-layer analysis methods may provide estimates of in-situ permeability to gas, fracture half-length, fracture conductivity, and drainage area, which are used in evaluating the success of a fracture treatment, selecting restimulation candidates, optimizing future fracture treatments, forecasting future performance, and estimating reserves.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".