The production profile of an unconventional resource play typically has a very steep decline after attaining the peak production rate. Consequently, operators are focused increasingly on drilling the wells faster and getting them on production quicker in order to improve early cash flows. This can sometimes come at the expense of gathering potentially useful data that may help improve reservoir characterization. This practice raises a couple of vital questions that traditional methods based on either deterministic or one-variable-at-a-time (OVAT) methods struggle to answer coherently. Examples of such questions include:
How much impact does drilling and completion speed have on the overall project economic measures?
What is the relative significance of all the key factors affecting the project economic measures?
Is it worth taking the time to acquire data that can ultimately reduce the uncertainty in the reservoir characterization and production performance?
What are the approximate models for the response variables?
A new workflow based on experimental design concepts has been developed to answer the above questions and tested using an unconventional shale oil resource. In this case study, we used the D-Optimal design table and evaluated the impact of a number of diverse factors ranging from speed of drilling, put on production (POP) time, production ramp-up, expenditure, product price to production type curve on the project economic measures. The results, for example, show that while a reduction in drilling and completion times may affect early production metrics, some other factors like production type curve have much more impact on the project's net present value (NPV) and Discounted Profitability Index (DPI).