Practical Technique to Identify Infill Potential in Low-Permeability Gas Reservoirs Applied to the Milk River Formation in Canada

Hudson, J.W.; Jochen, J.E.; Jochen, V.A.

doi:10.2118/59779-ms

Cited by 23 publications

(12 citation statements)

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“…Later, Voneiff and Cipolla (5) applied a similar method to analyze well location and production data in the Ozona field. The benefits of this approach have also been demonstrated in other applications (6)(7) .…”

Section: Introductionmentioning

confidence: 93%

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Parameter Sensitivity Study of a Statistical Technique for Fast Infill Evaluation of Mature Tight Gas Reservoirs

Guan

McVay

Jensen

2006

Journal of Canadian Petroleum Technology

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With the increasing demand for natural gas and higher prices, more and more gas basins within North America are undergoing infill drilling. It is not uncommon for a company to have hundreds or thousands of infill candidates to choose from. But quantifying the drilling or recompletion potential in large producing gas basins is often a challenging problem, due to large variability in rock quality, well spacing, and well completion practices, and the large number of wells involved. Complete integrated reservoir studies to determine infill potential are often too time consuming and costly for many large tight gas basins. In previous work, we evaluated the accuracy of a statistical moving-window technique that had been used in mature tight gas formations to assess infill and recompletion potential. We compared the moving window infill well predictions to those from reservoir simulation. Results indicated the technique accurately predicts the combined infill production estimate from a group of infill candidates, often to within 10%. Here, we report on a reservoir parameter sensitivity study and show that the accuracy of predicted infill performance decreases as heterogeneity increases and increases as the number of wells in the project increases. Also, the search area and well spacing have a large effect on the accuracy of estimates from the statistical method. Because of its speed, accuracy, and reliance upon readily available data, however, the statistical technique can be a useful screening tool for large infill development projects. Introduction Quantifying the drilling or recompletion potential in producing gas basins is often a challenging problem due to large variability in rock quality, well spacing, and well completion practices, and the large number of wells involved. Complete integrated reservoir studies to determine infill potential are often too time consuming and costly for many producing gas basins. Instead of conducting detailed studies, some investigators have used empirical or statistical analyses to model variable well performance(1–7). McCain et al.(4) used a statistical, moving window method to determine infill potential in a complex, low-permeability gas reservoir. Later, Voneiff and Cipolla(5) applied a similar method to analyze well location and production data in the Ozona field. The benefits of this approach have also been demonstrated in other applications(6–7). The technique employed in this work, herein referred to as Mosaic or moving window technique, is an extension of the method described by Voneiff and Cipolla(5). It is similar in that it consists of a multitude of local analyses, each in an areal window centred around an existing well (Figure 1). In this work, however, a more rigorous, model-based analysis is employed in each moving window. The model is based on a combination of the material balance equation and the pseudo-steady state flow equation, simplified by assuming that many properties are constant within an individual window. In a previous work(8), the moving window technique used here was found to accurately predict infill well performance for a group of infill candidates, often to within 10%, but the predicted infill potential for individual wells in the group could be off by more than 50%.

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Section: Introductionmentioning

confidence: 93%

“…Instead of conducting detailed studies, some investigators have used empirical or statistical analyses to model variable well performance (1)(2)(3)(4)(5)(6)(7) . McCain et al (4) used a statistical, moving window method to determine infill potential in a complex, low-permeability gas reservoir.…”

Section: Introductionmentioning

confidence: 99%

Parameter Sensitivity Study of a Statistical Technique for Fast Infill Evaluation of Mature Tight Gas Reservoirs

Guan

McVay

Jensen

2006

Journal of Canadian Petroleum Technology

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“…Our approach was to integrate the available data and use analytical tools and some small conceptual models. This is not unlike moving-mosaic techniques used for infill drilling (Voneiff and Cipolla 1996;Guan et al 2002;Hudson et al 2000;Hudson et al 2001;Wozinak et al 1997) or the quality-map approach (da Cruz et al 2004). A frequent approach to analytical simulation consists of modeling a well pattern (generally five-spot patterns), with the assumption that the obtained results represent the average performance of a particular sector of the reservoir.…”

Section: Field Cases Type I: Lack Of Data and Time Constraintsmentioning

confidence: 99%

Effective EOR Decision Strategies With Limited Data: Field Cases Demonstration

Manrique

Izadi

Kitchen

et al. 2009

SPE Reservoir Evaluation &Amp; Engineering

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Enhanced-oil-recovery (EOR) evaluations focused on asset acquisition or rejuvenation involve a combination of complex decisions using different data sources. EOR projects traditionally have been associated with high capital and operational expenditures (CAPEX and OPEX, respectively) as well as high financial risk, which tend to limit the number of EOR projects launched. We propose a workflow for EOR evaluations that accounts for different volumes and quality of information. This flexible workflow has been applied successfully to oil-property evaluations and EORfeasibility studies in many oil reservoirs. The method associated with the workflow relies on traditional (e.g., look-up tables, x-y correlations) and more-advanced (data mining for analog-reservoir search and geology indicators) screening methods, emphasizing identification of analogs to support decision making. The screening phase is combined with analytical or simplified numerical simulations to estimate full-field performance with reservoir-data-driven segmentation procedures. This paper illustrates the EOR decision-making workflow by use of field case examples from Asia, Canada, Mexico, South America, and the United States. The assets evaluated include reservoir types ranging from oil sands to condensate reservoirs. Different stages of development and information availability are discussed. Results show the advantage of a flexible decision-making workflow that can be adapted to the volume and quality of information by formulating the correct decision problem and concentrating on projects and/or properties with the highest expected economic merit. An interesting aspect of this approach is the combination of geologic and engineering data, minimizing experts' bias and combining technical and financial figures of merit. The proposed method has proved useful to screen and evaluate projects/properties very rapidly, identifying when upside potential exists.

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“…Our approach is to integrate the available data and use analytical tools and some small conceptual models. This is not unlike moving mosaic techniques used for infill drilling (Voneiff and Cipolla, 1996;Guan et al, 2002;Hudson, Jochen and Jochen, 2000;Hudson, Jochen and Spivey, 2001;Wozinak, Wing and Schrider, 1997) or the quality map approach (Da Cruz, Horne, and Deutsch, 2004). A frequent approach to analytical simulation consists of modeling a well pattern (generally 5-spot patterns), with the assumption that the obtained results represent the average performance of a particular sector of the reservoir.…”

Section: Case Study Cmentioning

confidence: 99%

Effective EOR Decision Strategies with Limited Data: Field Cases Demonstration

et al. 2008

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Enhanced-Oil Recovery (EOR) for asset acquisition or rejuvenation involves intertwined decisions. In this sense, EOR operations are tied to a perception of high investments that demand EOR workflows with screening procedures, simulation and detailed economic evaluations. Procedures have been developed over the years to execute EOR evaluation workflows.We propose strategies for EOR evaluation workflows that account for different levels of available information. These procedures have been successfully applied to oil property evaluations and EOR applicability in a variety of oil reservoirs. The methodology relies on conventional and unconventional screening methods, emphasizing identification of analogues to support decision making. The screening phase is combined with analytical or simplified numerical simulations to estimate full-field performance while maintaining rational reservoir segmentation procedures. This paper fully describes the EOR decision-making procedures using field case examples from Asia, Canada, Mexico, South America and the United States. The type of assets evaluated includes a spectrum of reservoir types, from oil sands to light oil reservoirs. Different stages of development and information availability are discussed. Results show the advantage of flexible decision-making frameworks that adapt to the volume and quality of information by formulating the correct decision problem and concentrate on projects and/or properties with apparent economic merit.Our EOR decision-making approaches integrate several evaluation tools, publicly or commercially available, whose combination depends on availability and quality of data. The decision is laid out using decision-analysis tools coupled with economic models and numerical simulation. This allows integrated teams to collaborate in the decision making process without over-analyzing the available data. One interesting aspect is the combination of geologic and engineering data, minimizing experts' bias and combining technical and financial figures of merit rationally. The proposed methodology has proved useful to screen and evaluate projects/properties very rapidly, identifying whether or not upside potential exists.

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Practical Technique to Identify Infill Potential in Low-Permeability Gas Reservoirs Applied to the Milk River Formation in Canada

Cited by 23 publications

References 3 publications

Parameter Sensitivity Study of a Statistical Technique for Fast Infill Evaluation of Mature Tight Gas Reservoirs

Parameter Sensitivity Study of a Statistical Technique for Fast Infill Evaluation of Mature Tight Gas Reservoirs

Effective EOR Decision Strategies With Limited Data: Field Cases Demonstration

Effective EOR Decision Strategies with Limited Data: Field Cases Demonstration

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