Assessing The Impact Of Prior Depletion On Future Inventory In The Delaware Basin

Objectives/Scope: As basin development matures, prior well depletion poses an increasing risk to the economic viability of future shale development. This study evaluates depletion characteristics in the Delaware Basin for both existing producing child wells and future inventory locations for six major operators. We quantify the impact of prior well depletion for each major operator and target formation in the basin, as well as the change in depletion trends over time.

Methods/Procedures/Process: To quantify prior well depletion, we sum the total fluid volume removed in the surrounding area of a shale well before its first production, and weight the prior removal by distance to the wellbore of interest. This depletion factor is then provided as an input to a machine learning model alongside variables representing interwell spacing, completion intensity, and geological setting. The machine learning model forecasts the monthly production profile up to 2 years. We then model both existing wells and approximately 80,000 remaining undeveloped horizontal well locations. The comparisons in this study will focus on the effect of prior well depletion on 2-year cumulative oil production in the Delaware Basin for both existing child wells and future developments across the Delaware.

Results/Observations/Conclusions: Our findings focus on the 6 largest operators in the Delaware Basin, and how they have managed their inventory to minimize the depletion of future locations. One operator has consistently operated in the highest rock quality, but faces the largest future depletion problem. Others have managed average rock quality to minimize future depletion, or have been able to overcome poor rock quality with large completions and low costs. Our machine learning outputs show both the prior strategies that operators have used to work around depletion, and where heavily depleted zones will be developed in the near future.

Applications/Significance/Novelty: These findings demonstrate the importance of prior depletion in assessing the quality of remaining inventory. Some locations that initially contained some of the best rock in the Delaware Basin are now merely average, and operators that have preserved their future inventory from depletion in average rock are now poised to be performance leaders. This approach also highlights the need for complex depletion calculations, along with the causal machine learning models to learn from these calculated values.