Seifert Subsurface Understanding, LLC
“Why project outcomes are often much below P50”
In my opinion, another reason is that we tend to anchor quickly on a “most likely” subsurface scenario, then build it out in detail using sophisticated and detailed static and dynamic modeling tools (“Integrated Reservoir Modeling – IRM”). We then vary some parameters to obtain a probabilistic range. For example, we may use a more optimistic/pessimistic top reservoir surface (perhaps even out of a probabilistic range), shift the porosity-permeability distributions up or down some and use some stochastic modeling for distribution. The result is a probabilistic outcome that revolves around the BTE (“best technical estimate”) which has effectively become the P50.
This procedure essentially represents evaluating the ‘random error’ of the data. Even deploying the more sophisticated and complex Experimental Design Approach is not solving this problem, it only evaluates the ‘random error’ better, by using a smarter way to sample the random uncertainty error space.
If, however, the reservoir were to be significantly different from the BTE that we anchored on earlier, e.g., if a reservoir is more channelized instead of a more lobate deposit, it is more proximally deposited than expected, a strong well bias exists or if effective permeability is much lower than rock permeability, due to diagenesis or preferential deposition, then shifting key parameters will not capture any possible alternative scenario. Such alternative and different scenarios would represent a ‘systematic error’, and such alternative scenarios would have to be built separately and evaluated separately. Building separate models for alternative scenarios is very time consuming, for the seismic interpreter, the static geo-modeler and certainly for the reservoir simulation engineer and, therefore, are often not done in the interest of predetermined tight timelines.
Two elements to significantly address this dilemma are proposed
First, develop a thorough and truly multi-disciplinary understanding of the reservoir. Using advanced data analysis, analytical simple modeling tools and ‘thought sharing’ in a truly integrated workshop setting will allow revisiting all data and previous interpretations, integrate all different disciplines (data & people) effectively, allow evaluation and testing for alternative scenarios and their screening for possible impact. One of the biggest wins of such workshops often is that the entire team walks away with a shared understanding and view of the reservoir and path forward.
Second, effective multi-scenario thinking (and modeling). In multi-scenario modeling, several alternative scenarios are evaluated in a very comprehensive way, using analytical and simple modeling methods to understand their impact and high-level ranges, before embarking on comprehensive 3D static/dynamic modeling path. Once 3D static/dynamic modeling is starting, better information exists to quantify the relevant subsurface scenarios, identify risks and develop and stress test a contingency plan for a realized high-risk outcome.
After all. designing a development scheme for the wrong subsurface scenario can be economically disastrous, e.g., if more wells are needed or if water injection is needed, or if fractures channel early water breakthrough.