Abstract
This study uses a machine learning framework to systematically analyze field production and completion data to understand the impact of frac-hits on parent and child wells and predict well spacing and completions design. Frac hits are one of the most pressing reservoir management issue that can enhance or compromise production over either the short-term or have sustained impacts over longer times. The extent of the impact is dictated by a complex interplay of petrophysical properties (high-perm streaks, mineralogy, etc.), geomechanical properties (near-field and far-field stresses, brittleness, etc.), completion parameters (stage length, cluster spacing, pumping rate, fluid and proppant amount, etc.) and development decisions (well spacing, well scheduling, etc.). As a result, the impact of frac-hits is not straightforward and difficult to predict.
The study uses data from the Meramec, Woodford and Wolfcamp formations. We develop an automated machine-learning based frac-hit detection algorithm that also quantifies the impact on the parent and child wells using matched decline curve models. We analyze about 500 parent and over 1100 child wells in the three formations. Our results show that the key factors governing the extent of the impact are the extent of depletion and producing oil rate of the parent well before frac hit, completion design parameters (fluid and proppant amount) and well spacing. Our machine learning analysis generates regression models to predict the impact of frac hits. These regression models are coupled with economic analysis to determine optimum spacing for any given completion design or optimum completion design for any given spacing.
The parent wells in all three formations had both positive and negative impact of the frac hits. Around 60–67% parent wells were negatively impacted while 33–40% wells were positively impacted. For the child wells, 71–85% wells were negatively impacted and 15–29% of the wells were positively impacted. Combining the impact on parent and child wells, the impact is dominated by the child wells as 69 to 82% of the parent-child pairs were negatively impacted and only 18–31% of the pairs were positively impacted. Considering percent loss in cumulative oil volumes in the next 5-years, in the Meramec, parent wells on average show a 16% reduction while child wells show a 39% reduction due to frac hits. The corresponding numbers for the Woodford formation are 19% and 37% and Wolfcamp formation are 20% and 22%, respectively. This translates to a parent well losing on average 40–50 thousand bbls in next five years and a child well losing on average 130–150 thousand bbls in the same period.
This study systematically analyzes available data to understand the impact of frac hits and formulates a machine learning-based well spacing-well completions matrix workflow that can easily be extended to other formations by integrating commonly available production and completions data.
