When Does a Longer Shut-in Lead to a Larger Radius of Investigation?
Bibliographic record
Abstract
When Does a Longer Shut-in Lead to a Larger Radius of Investigation? Steve David Ewens; Steve David Ewens Fekete Associates Inc. Search for other works by this author on: This Site Google Scholar Mehran Pooladi-Darvish Mehran Pooladi-Darvish U. of Calgary Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, September 2006. Paper Number: SPE-103608-MS https://doi.org/10.2118/103608-MS Published: September 24 2006 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Ewens, Steve David, and Mehran Pooladi-Darvish. "When Does a Longer Shut-in Lead to a Larger Radius of Investigation?." Paper presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, September 2006. doi: https://doi.org/10.2118/103608-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractWhile the concept of radius of investigation is better understood for drawdown tests, its applicability to buildup tests is less certain. For example, a rule of thumb is that "one cannot see a particular feature in a buildup unless the radius of investigation during the preceding flow period has seen that feature". In this paper, we clearly illustrate that the radius of investigation of a buildup can be larger than that of its previous flow period.Another common contention is that the radius of investigation of a buildup is limited by noise dominating the late time pressure behavior. Oliver1 and later Thompson and Reynolds2 defined the radius of investigation based on the distance from the well to the region of the reservoir which has the greatest impact on the pressure derivative. We have used this approach to calculate the derivative and show that the ratio of noise to the signal from the reservoir does not necessarily increase. We show that when data is sampled appropriately, the radius of investigation of a buildup can easily go beyond that of the preceding flow period, and clearly demonstrate when this may remain unaffected by noise.IntroductionRadius of Investigation is a well known, albeit poorly defined concept in pressure transient analysis. A pressure transient is created when a disturbance such as a change in rate occurs at a well. As time progresses, pressure transients advance further and further into the reservoir. The practical concept of radius of investigation does not address the particular behavior of the linear diffusivity equation which indicates an infinitesimal change in pressure everywhere in the reservoir, following a disturbance at the wellbore. The purpose of radius of investigation is to quantify the distance that a significant pressure change has advanced into the reservoir at any specified time. It is often defined as the furthest distance from the wellbore where there is a significant change in pressure due to a change in rate at the wellbore. The term significant is open to a wide range of interpretations and, as a result, there exists a variety of approaches to quantifying the radius of investigation (many have been summarized in Refs. 3 and 4).An alternate definition has been proposed1,2 and is based on the idea that the radius of investigation is the distance from the well to the region of the reservoir which has the greatest impact on the pressure data being measured at the wellbore. The pressure derivative plot is used to identify the dominant flow regimes during a test period. Therefore, the region of investigation can be defined as the portion of the reservoir which influences the pressure-derivative the most. Oliver1 derived a novel relationship between the permeability estimated from the pressure-derivative analysis and the region of the reservoir that affects the permeability estimate. Later on, Thompson and Reynolds2 presented a similar relationship between the magnitude of the pressure derivative and the permeability distribution within the reservoir. They showed that for a cylindrical reservoir with a single-phase slightly compressible fluid, the permeability estimate from a pressure-derivative plot is a harmonic average of the radial permeability distribution with a particular spatial weighting function. The weighting function represents that region of the reservoir where the flow rate is changing the fastest with respect to the natural-log of time. When a well is opened to flow, a pressure transient is created and this transient propagates throughout the reservoir, leading to pressure changes away from the wellbore. These pressure changes affect the fluid inflow, which in turn affects the measured pressure. It is this dependency between the measured pressures in the wellbore and the flow within the reservoir that allows the use of well testing for reservoir characterization.The advantages of the definition given in Refs. 1 and 2 are twofold. The criterion used to determine what is significant is that a feature at a distance from the wellbore must be observable on the pressure derivative plot. The second advantage is that it illustrates the region of the reservoir which influences the pressure derivative, i.e. the region of investigation. Keywords: pressure transient testing, buildup test, logarithmically, noise, Upstream Oil & Gas, drawdown transient, investigation, derivative, signal ratio, reservoir Subjects: Formation Evaluation & Management, Pressure transient analysis, Drillstem/well testing This content is only available via PDF. 2006. Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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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 | 0.028 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.077 | 0.017 |
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".