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Record W1983036809 · doi:10.2118/2009-103

A Method for Estimating Hydrocarbon Cumulative Production Distribution of Individual Wells in Naturally Fractured Carbonates, Sandstones, Shale Gas, Coalbed Methane and Tight Gas Formations

2009· article· en· W1983036809 on OpenAlexafffundabout
Roberto Aguilera

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCoalbed methaneGeologyOil shalePetroleum engineeringDrillingHydraulic fracturingNatural gasCarbonateMethaneFossil fuelPetrologyCoal miningCoalPaleontology

Abstract

fetched live from OpenAlex

Abstract A method, based on factual observations of naturally fractured reservoirs in several countries is presented for estimating distribution of hydrocarbon cumulative production in wells drilled in fractured reservoirs of types A, B or C. These observations indicate that in reservoirs of type C most of the cumulative production is provided by just a few wells while the majority of the wells contribute a small part of the reservoir cumulative production. In reservoirs of type B the number of wells contributing significantly to cumulative production becomes larger relative to the case of type C reservoirs. Finally in reservoirs of type A, a large number of wells contribute to field production, as compared with type B reservoirs. The method is shown to be useful for tackling problems of practical importance in naturally fractured reservoirs including, performing or not infill drilling, estimating the variation in cumulative hydrocarbon production per well in a given reservoir, and estimating the number of wells that might be required for a given field hydrocarbon recovery. The method is illustrated using data from various fractured reservoirs, including the Barnett shale and sandstone reservoirs in the United States, carbonate reservoirs in Mexico and Venezuela, and coalbed methane reservoirs and tight gas sands in Canada. Introduction Methods for estimating the optimum number of wells in a given reservoir have been available for over 80 years (Haseman,1 1929). More recently Nelson2 (2001) analyzed cumulative production per well in individual naturally fractured reservoirs and found that there are distinctive variations in the production distributions depending on the amount of natural fracturing and heterogeneity present in the reservoir. From this observation Nelson concluded that these distributions are a function of fractured reservoir type, something that has been corroborated by this author in several instances as discussed in this study. Figure 1 shows the ABC classification of naturally fractured originally introduced by McNaughton and Garb3 (1975). In naturally fractured reservoirs of Type A the storage capacity in the matrix porosity is large compared with storage capacity in the fractures (Figure 1A). This is generally equivalent to a reservoir of type 3 in Nelson's classification (2001). For this case, it can be seen in the lower part of Figure 1A that a small percentage of the total porosity is made out of fractures. In general, this situation would tend to occur in reservoirs where the matrix porosity is rather high (larger than 10 up to more than 35%). However, there are exceptions. For example reservoirs in tight gas formations can be generally classified as being of type A even if their porosity is usually smaller than 10%. If the matrix has some permeability so as to allow flow into the wellbore, Type A reservoirs can be considered equivalent to what Nelson (2001) has called "fracture permeability assist" reservoirs, i.e., reservoirs where the fractures contribute permeability to an already producible reservoir. Figure 1B shows a schematic of rocks with about the same storage capacity in fracture and matrix porosity (Type B reservoirs).

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.998

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 head0.011
GPT teacher head0.252
Teacher spread0.241 · 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 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".

Quick stats

Citations16
Published2009
Admission routes3
Has abstractyes

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