Bibliographic record
Abstract
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 191458, “Good Tests Cost Money, Bad Tests Cost More: A Critical Review of DFIT and Analysis Gone Wrong,” by R.V. Hawkes, SPE, Trican Well Service; R. Bachman, SPE, CGG; K. Nicholson, Perpetual Energy; D.D. Cramer, SPE, ConocoPhillips; and S.T. Chipperfield, SPE, Santos, prepared for the 2018 SPE International Hydraulic Fracturing Technology Conference and Exhibition, Muscat, Oman, 16–18 October. The paper has not been peer reviewed. Diagnostic fracture injection tests (DFITs) incur direct and indirect costs resulting from the tests themselves and the extended time required for the pressure falloff, which delays the completion of the well. The benefits, therefore, must outweigh the costs if the test is to be justified. These tests are performed regularly around the world because a DFIT is one of only a few processes that can help quantify both geomechanical properties and reservoir-performance drivers within the same test. Introduction Operators and service providers commonly experience problems with DFIT execution and analysis despite efforts to reduce errors and inconsistencies. Before any field execution or analysis, the objectives of a DFIT must be considered. Historically, DFITs were performed predominantly for the purpose of designing better full-scale hydraulic-fracture treatments with early-time measurements of initial shut-in pressure, leakoff coefficient, and fracture closure having priority over reservoir parameters such as permeability and pore pressure. Increasingly, practitioners are using DFITs to measure reservoir parameters such as initial pressure and permeability. While, in many cases, these parameters may be obtained from a single successful test, other situations have time constraints or rock and reservoir properties that constrain operations to a point where priorities must be set. While leakoff and closure values are determined early in the DFIT shut-in period, reservoir pressure and permeability are derived from late-time measurements that may require longer falloff times. The complete paper presents cases encountered in which test procedures/operations or incorrect analysis misled engineers. Cases presented are Several Canadian Duvernay shale wells illustrating the importance of multiple tests and the use of gradients to understand fracture orientation and possible complexity. A well where the initial DFIT had an injection rate that was too low combined with operational issues. A second test on the same interval yielded better results. A Canadian Montney well where rock/fluid interactions led to a false radial-flow signature. Two subnormally pressured Canadian oil wells where surface falloff pressure dropped to a vacuum (i.e., falling liquid level), causing late-time effects that were not reservoir related. The authors present a work flow to determine reservoir pressure in this situation. An Australian naturally fractured gas well showing the importance of sufficient falloff time. A proper DFIT may be critical for assessing the geomechanical and reservoir properties of unconventional reservoirs. However, simple guidelines such as wellbore conditioning, the understanding of pressure anomalies resulting from wells going on vacuum, and the importance of flow-regime identification are often overlooked, leading to poor results. Having access to numerous high-quality data sets from various international oil and gas operators provides insight to establishing some useful guidelines that are applicable anywhere in the world.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".