After Closure Analysis in Tight Gas Reservoirs: A Case History of Pre-Fracture Injection Tests Performed in the Brassey Field
Bibliographic record
Abstract
Abstract The design of a fracture treatment can vary and is dependent on parameters such as formation type, formation height, reservoir pressure, porosity and flow capacity (or KH). Most of these parameters can be determined prior to the fracture stimulation relatively easily at minimal cost using well logs, static gradients, DST's, cores, etc. The exception is flow capacity. Although it can be difficult and costly to determine prior to the fracture treatment, it could be considered the most important fracture design parameter. Another consideration is whether a zone should even be completed. Without an accurate idea of the formation flow capacity, it is difficult to decide prior to fracturing the zone if it will be worth the cost of the completion. This is especially true in tight gas formations where the economics of the zone can be marginal. If a zone can be determined to be un-economic prior to the fracture treatment, then significant cost savings can be realized by aborting the treatment. After Closure Analysis (ACA) is one method of easily determining a formation's flow capacity prior to the fracture treatment at minimal cost. ACA involves pumping a small volume of fluid into the formation above fracture pressure and recording the pressure falloff that follows. The pressure data can be analyzed, and once the radial flow regime is achieved, the formation's flow capacity can be determined. If the results from ACA are to be used for making completion decisions, then the method must first be proven to be practical and reliable. This paper examines field examples of ACA used on a zone in the Deep Basin that can be considered as tight gas. The ACA was completed using a software model and then compared with the results of post fracture pressure transient analysis. This was done to determine the accuracy of the ACA. Introduction Fracture treatment designs need to be tailored to the formation being treated. Parameters such as formation type, formation height, reservoir pressure and porosity can be determined using well logs, cores, static gradients, etc. One of the most important formation parameters is the flow capacity (KH) which can be costly and time consuming to determine Although most wells are successful and produce at economic rates, others do not produce or produce at uneconomic rates. In reality, many wells are stimulated without knowing if they will be economic. As the industry develops tighter gas wells, an increasing number of wells are shown to produce at un-economic rates and volumes of gas after considerable time and money have been invested in their completion. If, prior to stimulating, it can be determined that a zone will be un-economic, the treatment can be aborted and the cost savings used to develop more economic wells. The formations flow capacity (KH) is one measure that can be used to determine a well's productivity prior to fracturing. After Closure Analysis (ACA) is a relatively easy method of determining a formation's KH prior to fracturing compared to over well tests.1
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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 | 0.000 |
| 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 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".