Shale gas currently provides 20% of domestic supply, is targeted by half of the gas-directed drilling rigs, and represents the large majority of domestic resources. However, modern shale plays, their development strategies and their engineering analysis are young by comparison to those of conventional reservoirs. Uncertainty in shale gas reserves has significant implications at both the micro and macro levels.Conventional reservoir engineering tools must be viewed as potentially inadequate (or even inappropriate) for the evaluation of shale gas performance primarily because of the extremely low aggregate permeability of these systems, but also because of other unique aspects of the systems. Reservoir modeling (simulation) has an important role as an assessment and prediction tool; however, the character of the reservoir (induced and enhanced natural fractures) must be considered, as well as the geological and fluid characteristics. Rate-transient analysis (modern decline analysis) techniques are also more rigorous and have been expanded and adapted to fit the uniqueness of shale gas production. Application of each method for shale gas is discussed, including methods and limitations. These two techniques more closely represent the physics of shale gas production, but their implementation is often prohibitive.By way of necessity, much engineering evaluation is performed using Arps decline curve analysis. This technique is argued by some to be inappropriate due to a lack of theoretical support and demonstrated tendency to over-estimate reserves in tight gas systems. Given the limitations, practical methods exist to reduce error associated with its use. A newer decline method, power-law exponential, is also investigated.
TX 75083-3836 U.S.A., fax 01-972-952-9435. AbstractThe chief use of probabilistic methods is to assess risk and opportunity, making them most applicable to situations of significant uncertainty. Hence, the cardinal sin unique to probabilistic analysis is to underestimate the range of outcomes. Unfortunately, the situations of the greatest uncertainty are also the ones where poor judgment is most likely to create unreliable results and dangerous decisions. The best judgment in probabilistic analysis is that which recognizes the full range of uncertainty by carefully framing the problem and by avoiding pitfalls which artificially reduce the range of results.
Decline curve analysis has dominated reserve estimation techniques for decades. Ironically, the introduction and expansion of probabilistic calculation techniques largely by-passed these kind of calculations, but not for lack of effort. Much thought and writing has been dedicated to the unique issue of defining uncertainty in future production trends. Despite two decades of slow evolution, the resulting methods have been partial and prohibitive. Methods like scenario analysis, Delphi forecasting, bootstrapping and Bayesian networks have been proposed and even sometimes utilized. Though they have demonstrated some success, they all suffer from limitations in difficulty and objectivity and aggregation. They have also all utilized smooth model fits and projections. In tandem, other kinds and ways of uncertainty analysis have been applied in the time-domain or dependent sequences using decision trees, Bayesian networks and, more recently, Markov chains. Newly developed machine-learning techniques combine these two separate streams of uncertainty analysis into a framework that promises to address both the quantification and aggregation of uncertainty in an objective way. What seems like a black-box method is instead a natural and automated extension of well-accepted techniques, and the few published studies of the technique suggest that it may be robust, particularly over the scale of years.
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractMany papers have been written concerning the energy industry's need to become more efficient in its methodologies and operations. The shrinking qualified workforce and global economic factors are placing further pressures on the energy sector to streamline processes. This requirement extends itself to all phases of the energy industry, especially with multidisciplinary multi-organizational teams. 1-5
The current SPE/WPC definitions for reserves quantified by probabilistic methods are ambiguous and largely inconsistent with deterministic criteria. Probabilities are least well defined and most subjective at the extremes of the ranges of outcomes. Since reserve definitions are meant to provide consistency across evaluators and evaluations, the current definitions are not workable. When defining reserves, volumes should first be classified into a category. A probabilistic analysis can then be made of the uncertainty in each category, and a central estimate of Proved reported. When probabilistic analyses are used to determine value, the same restraints are not necessary, but the same wariness of the extremes of the distributions should be applied.
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