Effect of Real Time Measurements and Analysis on Carbonate Stimulation in Horizontal Open Hole Producers - Lessons Learned at Eocene Field in the Partitioned Zone
Bibliographic record
Abstract
Abstract Several unknowns still remain in carbonate matrix acidizing. Poor correlation between porosity and permeability in carbonate formations, the possibility of natural fractures and limitations in the economically practical number of core samples often result in a largely unknown permeability variation across the pay. Intelligent fluid systems have been developed to address such uncertainty, but the lack of real time downhole data during the treatment typically prevent confirmation of the fluid system efficiency. For well bore access, coiled tubing intervention often provides a quick and economically attractive alternative to the work over rigs, but both types of intervention have always been limited with their ability to provide downhole data. Typically, for both coiled tubing and workover interventions, a reliance on surface measurements to make inference about the downhole conditions has been used. However, mechanical and chemical processes in the near wellbore, downhole fluid movements and the above mentioned unknowns in reservoir properties make surface readings a poor estimate of true downhole conditions. New downhole measurements provided with fiber optic enabled coiled tubing (FOECT) attempts to address some of these limitations. This novel approach deploys a downhole sensor package with fiber optic telemetry through a protective umbilical inside the coiled tubing string. The downhole sensor package provides real time temperature, pressure and depth readings with a casing collar locator, allowing the operator quantitative feedback of downhole conditions during treatment. The use of fiber optic telemetry additionally allows recording of distributed temperature surveys (DTS) for obtaining high resolution temperature profiles across the entire wellbore. Combined interpretation of the real time downhole data and the DTS profiles enables real time feedback during and between the different stages of carbonate openhole stimulation. With DTS, fluid placement as well as changes to the injectivity profile can be interpreted at key stages of the treatment and necessary changes to the planned treatment can be implemented accordingly. This paper outlines recent case histories, where for the first time in Kuwait fiber optic enabled coiled tubing was used to optimize stimulation treatment for the operator. These candidate wells were sub-hydrostatic pore-pressured horizontal openhole producers, completed with an electrical submersible pump (ESP) for artificial lift. The DTS system enabled the operator to identify both high permeability zones as well as tight zones across the entire openhole section. This enabled the operator to take pro-active decision on where to spot diverter and acid during the treatment. This new and modified approach to stimulation not only helped in improving production but also resulted in marked changes in treatment volumes as dictated by the DTS measurements. The implementation of this fiber optic enabled coiled tubing workflow had a significant impact on the operator’s confidence with carbonate matrix acidizing, as very limited downhole data was available for informed decisions to optimize stimulation treatments. As such, the downhole data obtained from the fiber optic enabled coiled tubing stimulation not only helped in optimizing the stimulation treatments but also help the operator to get a better understanding of the reservoir.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".