Abstract
The exploratory phase of a hydrocarbon field is a period when decision-supporting information is scarce while the drilling stakes are high. Each new prospect drilled brings more knowledge about the area and might reveal reserves, hence choosing such prospect is essential for value creation. Drilling decisions must be made under uncertainty as the available geological information is limited and probability elicitation from geoscience experts is key in this process. This work proposes a novel use of geostatistics to help experts elicit geological probabilities more objectively, especially useful during the exploratory phase. The approach is simpler, more consistent with geologic knowledge, more comfortable for geoscientists to use and, more comprehensive for decision-makers to follow when compared to traditional methods. It is also flexible by working with any amount and type of information available. The workflow takes as input conceptual models describing the geology and uses geostatistics to generate spatial variability of geological properties in the vicinity of potential drilling prospects. The output is stochastic realizations which are processed into a joint probability distribution (JPD) containing all conditional probabilities of the process. Input models are interactively changed until the JPD satisfactory represents the expert’s beliefs. A 2D, yet realistic, implementation of the workflow is used as a proof of concept, demonstrating that even simple modeling might suffice for decision-making support. Derivative versions of the JPD are created and their effect on the decision process of selecting the drilling sequence is assessed. The findings from the method application suggest ways to define the input parameters by observing how they affect the JPD and the decision process.
Highlights
When oil & gas companies explore or develop new hydrocarbon reserves, they must plan and decide the order in which the prospects and appraisal wells will be drilled
The processing options provide alternatives for building the joint probability distribution (JPD) that avoids assigning zero probability to events given the limited number of samples from the stochastic dataset
The work presented here shows a novel method to elicit probabilities for value creation and decision-making support in the context of hydrocarbon exploration. It is based on a geostatistical process that “translates” geological expert knowledge into probabilities through the creation of conceptual models representing possible subsurface bodies
Summary
When oil & gas companies explore or develop new hydrocarbon reserves, they must plan and decide the order in which the prospects and appraisal wells will be drilled. In the exploratory phase of a hydrocarbon field, the information available for the decisions is scarce, often limited to seismic interpretations and data from analogous fields. Drilling the pioneer well opens a new dimension of information: well-data. It has a high cost but is fundamental in confirming oil presence, understanding the geology, correlating it with the seismic response, and estimating reserves. The improved understanding that comes from specific welldata might impact future drilling choices by changing the decision maker’s (DM) beliefs about the likelihood of uncertain outcomes from each prospect. Solving it defines the course of action that yields the highest expected value (EV) for the decision metric, often being the net present value, commercially producible reserves, or volume of oil in place
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
