Economic Decision Making and the Application of Nonparametric Predition Models
- Emil D. Attanasi (U.S. Geological Survey) | Tim Coburn (Abilene Christian University) | Philip Freeman (U.S. Geological Survey)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- December 2008
- Document Type
- Journal Paper
- 1,089 - 1,096
- 2008. Not subject to copyright. This document was prepared by government employees or with government funding that places it in the public domain.
- 4.3.4 Scale, 4.6 Natural Gas, 6.1.5 Human Resources, Competence and Training, 5.8.7 Carbonate Reservoir, 5.7.5 Economic Evaluations, 2 Well Completion, 4.1.5 Processing Equipment, 1.6 Drilling Operations, 5.8.2 Shale Gas, 1.6.1 Drilling Operation Management, 4.1.2 Separation and Treating
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Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly.
Sustained increases in energy prices have focused attention on the development of marginally economic resources such as natural gas in low-permeability shale or in coal. The daily deliverability of these resources from individual wells is often relatively small, whereas the estimates of the total volume of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources are, by nature, areawide because profitable extraction requires the optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper demonstrates how simple nonparametric-regression-model predictions of technically recoverable unconventional gas resources at untested sites can be used with economic models to compute, at the regional scale, the costs of developing and producing such resources in partially developed areas. The predictive models are described in the next section. Following this description, the data, prediction results, and the predictive distributions of recoverable gas volumes are discussed. The assumptions and fundamental components of the economic analysis are then presented. The paper also demonstrates the benefits of applying the model predictions in the strategic ordering of drilling prospects and the benefit of updating predictions when the results of new drilling become available. The cost models provide a way to evaluate the economic payoff associated with the application of local prediction models. A worked example, that used data from the Devonian Antrim-shale gas play of the Michigan basin, provides the context for testing the usefulness of the local prediction models at the regional scale and also for strategic drilling decisions. In summary, the analysis shows that these models can be applied usefully to assess the regional economic potential and, at the strategic level, to rank prospects by order of value in partially developed areas.
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