Acid Fracturing and Hydraulic Fracturing Applied in a Single Well for a Deep Carbonate Reservoir Appraisal; The Completion, Perforation, Stimulation and Data Gathering Experiences for 7 Zones
Bibliographic record
Abstract
Abstract Acid Fracturing and Hydraulic Fracturing were used to stimulate and appraise a very challenging deep, low-porosity, carbonate reservoir with a complex combination of matrix background fractures (BF), and some large fault corridors and fault associated fractures (FAF). The well was drilled deviated through the reservoir to increase probability of intercepting fault corridors for production. The execution of the stimulations proved challenging due to the high strike-slip stress regime. Data from the pre-stimulation diagnostic tests was meticulously used to successfully plan and optimize each stimulation. Acid stimulations on previous 4 wells showed challenges in formation breakdown as well as potential for shear-dilation pre-existing fractures during the injection phase. While during the production phase a rapid collapse of fracture conductivity was observed. Therefore, proppant stimulations were selected to ‘preserve’ the conductivity increase from the fracture dilation. For the larger fault zones, high leak-off was expected and acid fracturing was applied with emphasis on etching of the fracture surfaces and maximizing the acid diversion. Design preparations included laboratory conductivity testing of acid recipes and proppant on outcrop material. Pre-stimulation diagnostic tests were designed for the acid and the proppant fracs, considering the different reservoir characteristics of each stimulation target. After the well was drilled, an integrated evaluation of mud logs, wireline logs, MPD drilling data and structural geology modeling was performed and 7 stimulation targets were identified. Acid stimulations were chosen for zones where faults and "fault-associated fractures" were identified and high leak-off was expected. The designs incorporated several diversion techniques that were successfully implemented to improve stimulation effectiveness. Proppant stimulations were placed in zones where only "background fractures" predominated, and initially followed designs typically pumped in unconventionals. The proppant stimulations, however proved to be the most challenging, and the pumping designs and perforation strategy were changed substantially after DFIT analyses. One hybrid stimulation treatment was conducted to create an etched fracture and then tail in with a proppant treatment at the end of the stimulation. This practice is rarely attempted in the oil and gas industry but here the feasibility is demonstrated. Intermediate clean-up and well testing were done to understand the reservoir characteristics and quantify whether acid fracturing or proppant fracturing is an appropriate stimulation method for the different zones in the reservoir. A small group of 7 seismic stations were deployed to track the area natural seismicity and monitor the stimulation events. The quality of diagnostic information exceeded expectations, and the results could be linked to the performance of the stimulations. It showcases the feasibility of utilizing only a small array of geophones to diagnose the stimulation effectiveness. Learnings and experiences for designing and execution of proppant fracturing, as an alternative to matrix or fracture acidizing in these complex carbonate formations are presented. Learnings for, successful diversion practices for acid fracturing in faulted carbonate zones with high leak-off are also shared.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".