Solutions for Better Production in Tight Gas Reservoirs Through Hydraulic Fracturing
Bibliographic record
Abstract
Abstract The challenge to make best producers for the least investment in tight gas reservoirs has always been with the oil and gas industry since production in many tight gas reservoirs is oftentimes marginal, at best. This paper presents solutions for better production in tight gas reservoirs through hydraulic fracturing. Properly engineered hydraulic fracture treatments are enablers to achieve overall economies of scale with development of tight gas reservoirs. These treatments are conducted to bypass completion damage and stimulate production from low permeability reservoirs. They are designed using simulators with a range of capabilities in an effort to maximize the economic benefit of the treatment, effective fracture length, and number of zones producing, fracture conductivity, acceleration of recovery, addition of reserves, and minimize job failures and treatment costs. To accomplish these goals, substantial amount of information is required to describe reservoir flow capacity and provide the data needed to predict treatment pressure response as well as fracture geometry and conductivity. These data will determine the optimum size of the treatment, the maximum proppant concentration that can be pumped, and the expected production response to the stimulation. When sufficient input data are available to characterize the reservoir, the fracture geometry can be accurately modeled with a capable simulator, the treatment goals listed above can be realized and an optimum design can be reached. The design process, including selection of proppants and fluids, pumping schedule, injected proppant concentrations, total job size, pump rate, and other parameters requires an idea of the desired outcome of the job: required fracture length, possible pack concentration and clean-up time. Critical measurements from testing of actual cores has allowed to sift through the chaff to find those "gems" in hydraulic fracturing that materially improve the completion efficiency in tight gas reservoirs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".