@proceedings {51255,
	title = {A management decision tool for ranking oil sands resource development opportunities},
	year = {2014},
	note = {IN:  SPE Hydrocarbon Economics and Evaluation Symposium, Houston Texas May 19-20, 2014},
	month = {05/2014},
	pages = {1 page },
	address = {Houston, TX},
	abstract = {A management decision-making and planning tool has been developed to provide a quick, high-level resource quality assessment of oil sands assets to enable economics-based ranking of investment opportunities.   It is systematic and transparent, largely avoiding the subjectivity and human bias often associated with ranking assets for capital allocation.

The model utilizes the available reservoir characterization information (API gravity and petrophysical analysis including oil saturation, effective porosity, V shale, pay thickness), expected operating conditions (steam injection pressure, horizontal well lengths), and a reservoir risk assessment to predict the key performance metrics for an in situ oil sands project using SAGD (steam assisted gravity drainage) including oil rates, steam-oil ratio and recovery factors.  The reservoir risk factor is a quantification of the production impact (lower expectations and/or increased uncertainty) from reservoir impairments based on expert opinions and reservoir simulation.  These performance metrics can then be used to estimate expected overall economic potential (IRR) for a given asset.  The ranking can be done at various levels: land sections, defined prospects, wellpad drainage areas, or at the individual (delineation) well level.

Predictive analytics techniques, in this case multi-variable linear regression, were used to construct the model. It was initially based on thermal recovery theoretical models for the SAGD process for predicting oil rates and a simple energy balance for predicting steam-oil ratio (SOR).   Subsequently it has been updated via industry production data {\textquotedblleft}fitting{\textquotedblright}, or applying the actual performance data of various mature, operating wellpads to improve the confidence level of the model.  The result is a hybrid model; science-based but influenced by real operating and production experience.

It has served as a primary tool used for strategic planning, in the setting of high-level performance targets (and probabilistic distributions thereof) for each of the assets.  This tool has enabled a resource driven development strategy, allowing the company to focus technical resources on the assets that possess the greatest economic potential.    Resulting business decisions include capital allocation (for additional delineation data) and more rigorous technical efforts (reservoir modeling and simulation) on the highest ranking prospects.},
	keywords = {economics, in-situ, model, modeling, planning},
	author = {Dlugan, M. and Pompa, A.}
}
