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Kurt Rudolph - Estimating, Benchmarking, and Managing Subsurface Uncertainty: Principles and Examples

AAPG Distinguished Lecture Series, 2023-24 Season

AAPG Distinguished Lecture Series, 2023-24 Season


A Distinguished Lecture talk given by Kurt Rudolph during 2023-24 AAPG DL Season.

Subsurface risk and uncertainty are recognized as very important considerations in petroleum geoscience. And even when volume estimates are relatively accurate, the reservoir characteristics that determine well placement and performance can remain highly uncertain. In analyzing results and work practices, three aspects of uncertainty are reviewed here.

Measuring Uncertainty. First, by comparing predictions to results in exploration wells, we can calibrate expectations and refine future predictions. Based on a database from ExxonMobil of 553 wildcats and 204 field development/redevelopment projects, median volume errors are ±40% for successful wildcats and ±20% for field developments. Risk is also significant in rank exploration wildcats, with a mean economic success rate of ~30%. At a parametric level, the inferred p10/p90 ranges for predictions are ±20-25% for net/gross and +/- 4 porosity units.

In this study, overall risking and volumetric results were generally unbiased, if very uncertain on an individual asset basis. Several examples are used to illustrate the challenges associated with these efforts.

Context matters too. For example, exploration play maturity has a strong influence on performance.  New play tests averaged a low rate of success (about 20% technical and 10% economic), but large success case volumes. Generally, chance of success increased and prospect success case volumes decreased with play maturity. For very mature plays, success rate decreased again. So, optimizing the efficient frontier, on a risk-reward basis, ensures a diverse and successful portfolio.

Seismic technologies also have had a significant influence on success rates. Wildcats drilled based on 3D seismic data had about a 10% higher success rate than those based on 2D data.  DHI-based prospects had about double the success rate of non-DHI-based prospects, with success somewhat under-predicted.

Geological Complexity. Second, prediction uncertainty is dependent on prospect, play, and basin complexity. High complexity can lead to unexpected results, and can only be cured with a sustained effort. When a complex basin (Gulf of Mexico) is compared to a simple basin (West Siberia), the creaming curves (discovery histories) are much different. The Gulf of Mexico has a much longer creaming trend, with multiple source rocks, a complex structural and stratigraphic evolution related to halokinesis, along with seismic -imaging challenges. This results in multiple plays, that are only understood over decades. High residual uncertainty means that complex opportunities can yield late-life value, but at a cost. In contrast, simple opportunities often put a premium on early entry (and, sometimes, early exit).

Integrating Uncertainty into Economic Analyses. Third, for improved decision making, incorporating subsurface uncertainty into economic evaluations is critical. Expected Monetary Value (EMV) is a particularly useful approach to address uncertainty. This is illustrated with a West Africa example (Girassol). EMV uses multiple probability-weighted scenarios to yield an improved characterization of the mean and range of economic outcomes. Moreover, using as few as three scenarios can yield similar results that are similar to a full probabilistic economic analysis of Net Present Value and Internal Rate of Return.

A special application of EMV is to evaluate the economic attractiveness of options. This includes measuring the benefit of collecting additional data (Value of Information) or in securing future choices such as a seismic option or development contingency (Real Option Analysis).

Work Models and Strategies. Last, avoiding bias is even more important than minimizing error. Programmatic overestimation of success rates and volumes can put a company out of business. Conversely, underestimating can lead to significant missed opportunities. In exploration, one should expect to be disappointed; but you should never be surprised. Three work practices can minimize bias and surprises:  

  • Ongoing monitoring of predictions versus actual results (benchmarking and stewardship), including discussion at the company through team level.
  • External independent peer reviews at key milestones, with reporting to management.
  • Probability-based uncertainty analysis of technical and commercial outcomes tied to discrete scenarios. The early articulation of scenarios is especially useful to capture alternative views, address conflicting information, and avoid cognitive anchoring.

In the News

Distinguished Lecturer


Kurt Rudolph

Adjunct Professor, Univ. of Houston and Rice University

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