Selection of Stimulation Fluids and Treatment Design for Low-Permeability Reservoirs
Bibliographic record
Abstract
Abstract From the inception of commercial hydraulic fracturing, using first gelled napalm and then thin fluids, until today, a half century later, the selection of the optimum stimulation fluid and treatment design continues to be highly controversial. From a theoretical standpoint, a long, conductive fracture is required to effectively recover reserves in ultra low permeability reservoirs.1 The 1980's and 90's saw the use of massive hydraulic fracturing treatments with up to five million pounds of proppant in crosslinked gel fluids employed to achieve the desired results. Many of these reservoirs are deep and hot, and service companies expend a significant effort to develop fluids with adequate rheological stability to pump long jobs at temperatures in the range of 250 - 350°F. Traditionally, gelled fluid formulations are selected based on the viscosity stability measured by Model 50 Fann viscometer studies.2 Fracturing fluids are marketed based on viscosity stability and seldom, if ever, is the fracture conductivity and cleanup information available for the fluid formulation alternatives to assist in the decision regarding fluid selection. Since the inception of the development of these types of fluids, industry experts voiced concerns about fracture cleanup3 and more recent studies demonstrate that filtercakes from crosslinked gels do not thermally decompose at 350°F.4 However, this strategy with viscous gels was not universally successful, and in some reservoirs, most notably the Austin Chalk5,6,7 and the Barnett Shale8,9 large "water fracs" pumped at high rate with up to 30,000 barrels water and only token amounts of proppant reportedly emerged as the economic treatment of choice.10,11 This strategy is not a new approach but a revival of the use of high rate water/sand treatments which proved to be very successful in a number of reservoirs years earlier.12 Reservoir discontinuities and complex dual porosity reservoirs certainly contribute to the complexity of developing the optimum treatment strategy. Natural fractures promote screenouts, and an industry trend to attempt to minimize screenouts by using overstabilized fluids developed. Unfortunately, using overstabilized fluids can seriously impact the conductivity of natural unpropped fractures and the proppant pack.13 Prefrac injection/fall-off tests proved to be one of the tools that can be used to identify the reservoir characteristics and assist in the treatment design.14 With a reliable measure of expected productivity, one can then imply completion efficiency. Traditionally, fluid design for completions in the 180-220°F reservoir temperature range remains problematic. We are aware of a number of cases where post-frac performance just does not meet expectations. In a study in the deep Upper Morrow in the Anadarko Basin, 17% of the wells produce less after frac than before.15 In this paper we will describe the use of prefrac injection/fall-off tests to characterize reservoir potential and compare the stimulation treatment proppant placement issues, treatment design, fluid formulation and production response for a number of producing horizons in the Deep Basin in Northern Alberta.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".