A Method for Estimating Hydrocarbon Cumulative Production Distribution of Individual Wells in Naturally Fractured Carbonates, Sandstones, Shale Gas, Coalbed Methane and Tight Gas Formations
Bibliographic record
Abstract
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).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".