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Record W2056082930 · doi:10.2118/2006-187

Evaluating Reservoir Aspects From Database on Infill/Step-Out Wells

2006· article· en· W2056082930 on OpenAlexaboutno aff
S.J. Springer, A.K. Singhal

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsInfillComputer scienceGeologyPetroleum engineeringDatabaseEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Abstract About 200 000 infill/step-out wells have been drilled in the Western Canadian Sedimentary Basin (WCSB) since 1980. This wealth of data implicitly included clues to various reserve characteristics but techniques for evaluation were not obvious to us. We tried different ways of processing data for deriving meaningful insights. In this paper we present some of these with appropriate examples. Pools were grouped by major formation type, fluid type, and depletion mechanism. The distribution performances of various sub-groups were individually evaluated. Where possible, performances of horizontal and vertical wells were treated separately. We also compared the value of the Average and the Median performance parameters. In most cases the average was much larger than the median. We prepared frequency plots and their representation on the normal (standard deviation) scale for the wells in each group. From these plots we were able to characterize the lateral heterogeneity of the reservoirs. This could be used in applying risk factors to future projects. Risk analysis can also be aided by other features of frequency distributions for corresponding wells. A rate-cumulative production plot was also helpful in determining acceleration benefits and incremental reserves resulting from infill drilling. Introduction Two hundred thousand infill and step out wells were drilled in the WCSB between 1980 and 2005 (Table 1a). Infill wells for oil and gas were approximately equal in numbers. About 75 percent of the wells were drilled in sandstone formations and 25 percent in carbonate formations. Overall, 50 000 wells were drilled in sandstone heavy oil reservoirs and 80 000 in sandstone gas reservoirs. Of these, more than 50% were shallower than 500 meters. In this paper we focused mainly on the performance of infill wells in mature light oil carbonate reservoirs. First, we reviewed the performance of two Mississippian pools in SE Saskatchewan (Williston Basin). Here, the horizontal wells performed noticeably better than the contemporary vertical infill wells. In Alberta however, in the three carbonate pools reviewed, two oil pools and one gas pool, the vertical infill wells performed better than the horizontal wells. (In the case of the Swan Hills BHL pool the difference was small) We also reviewed the performance of a heavy oil sandstone reservoir and a shallow sandstone gas reservoir. In both pools, the horizontal infill wells performed better than the vertical wells. A major objective of this paper is to show how a relatively simple statistical analysis of the performance curves could provide useful insights; and also, to present a method to qualitatively/quantitatively characterize the lateral heterogeneity as well as, risks in developing similar pools by infill drilling. In addition, we also illustrate how the oil rate-cumulative production plot could be used to identify contributions due to acceleration and incremental recovery. We have incorporated some of the ideas discussed in previous papers2–8. METHODOLOGY Our main data sources were the provincial Oil and Gas Reserves Books. Production data were obtained from a commercial data base. An "in-house" Excel based statistical program was used for analyzing/sorting the data and developing various statistical parameters, tables and graphs.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score1.000

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)0.0010.000

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 head0.049
GPT teacher head0.308
Teacher spread0.260 · 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.

Study designSimulation or modeling
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".

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Citations0
Published2006
Admission routes1
Has abstractyes

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