[URTeC 2023] Understanding the Drivers of Parent-child Depletion: A Machine Learning Approach​ (ID 3862321)

Understanding the Drivers of Parent-child Depletion: A Machine Learning Approach

Technical Paper Details::
Theme 5: Geomechanics – The Intersection of Geoscience and Engineering
Topic: Understanding the Drivers of Parent-child Depletion: A Machine Learning Approach
Authors: Dillon Niederhut*1, Alexander Cui1, 1. Novi Labs.

Abstract:

Every year, an increasingly larger fraction of drilling is done on pads or in zones where wells are already present. These existing wells have implications for the hydrocarbon production of any new developments, and therefore also the economic value of engaging in this activity. Here, we use a non-linear and multivariate machine learning approach to provide descriptive evidence of the effects of existing well production on infill wells and segregate that impact into the contributions of individual features. We find that the percentage of total reserves produced by existing wells before an infill well is brought online is the single strongest factor in determining the relative performance of the infill well as compared to the parent. Distance to the closest parent produces a nonlinear effect on child production, and one which is mediated by the percent of reserves already generated by the parent well. Finally, we observe mixed results for the influence of geology, which warrants further investigation.

Complete the form below and we will email the paper straight to your inbox.

First name(Required)
This field is for validation purposes and should be left unchanged.

Meet Novi Labs' knowledgeable experts.

Invite to Speak

Our team of experts is passionate about sharing valuable insights on US energy. Please provide us some information about your event, and we will get back to you as soon as we can

Date
This field is for validation purposes and should be left unchanged.