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Record W2069698345 · doi:10.2118/116087-ms

Reconciling Production and Well-Test Data in Mapping kh Trends: Field Study

2008· article· en· W2069698345 on OpenAlexaff
Mars Khasanov, Vitaly Krasnov, А. Г. Петров, Mohan Kelkar, Asnul Bahar

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2008
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsKerr Wood Leidal Associates (Canada)
Fundersnot available
KeywordsComputer scienceInterpolation (computer graphics)Lift (data mining)Production (economics)Field (mathematics)Data miningTest dataArtificial intelligenceMathematicsImage (mathematics)

Abstract

fetched live from OpenAlex

Abstract During a field development process, one of the parameters used to decide the in fill well locations is permeability-thickness (kh) map. This map can provide us with overall trends in the conductivity of the formation as well as information about optimizing water flood patterns. Rosneft routinely uses kh maps as a reservoir management tool. In addition to developing the well locations, it also uses the kh values to optimize the artificial lift design so that the wells are produced efficiently. One difficulty typically observed in generating kh maps is a prominent display of "bull's eyes." The values of kh can change dramatically from well to well, which causes problems in interpolation of these values. Some times, because of large discrepancies, the overall patterns are hard to discern and well planning is more difficult. In the proposed work, we developed a procedure for capturing the trends in kh maps by removing the bull's eyes. The kh values are determined by two methods: use the production data and using simplified procedure, determine the value of kh, or evaluate well test data and determine the kh values. In the first step, we developed a process of reconciling the well test data with the production data by adjusting the kh values and skin factors so that the productivity index can be maintained. In the second step, we assumed that uncertainty exists in determining the true kh value at each well location due to interpretation and resolution of data. Instead of strictly honoring the kh values at each location, using error kriging approach, we recalibrated the kh values so that the new kh maps are smoother and without bull's eyes, and are able to define the overall trends much better. We also ensured that the productivity index matches correctly. Further, by examining the productivity index as a function of time (based on production data), we are able to determine how the skin factor changes as a function of time, which provides valuable information about potential damage at the well location. The procedure was validated by applying it to a large oil field located in Siberia with successful application of in-fill well program.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.306
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2008
Admission routes1
Has abstractyes

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