[URTeC 2020] Decomposition of Publicly Reported Combined Hydrocarbon Streams using Machine Learning in the Montney and Duvernay (ID 2795)

Canadian operators have an information problem: in the public data, “gas” wells don’t have to report their liquids content. This means that nearly the entire revenue stream may be unknown — a tough problem for competitor surveillance, design optimization, and even acreage evaluations. In this paper, we present a workflow that uses geology, completions, spacing, and the reported recombined stream to estimate CGR and liquids content — with big accuracy improvements over traditional methods.



Decomposition of Publicly Reported Combined Hydrocarbon Streams using Machine Learning in the Montney and Duvernay.

CGR estimation errors for the Machine Learning method in this paper (red), compared to dew point or initial CGR methods.


Publicly reported hydrocarbon production data offers the opportunity to assess new acreage, compare production with privately held wells, and to develop general insights about hydrocarbon plays. However, in some cases, publicly reported data obscures valuable information due to reporting requirements and procedures. For example, in Canada, retrograde condensate reservoirs produce gas and condensate, but the volume is often reported as total gas-equivalent hydrocarbon volume. Our goal in this study is to deconvolve production histories of aggregated gas-equivalent hydrocarbon volumes into separate production histories for the condensate and gas streams. We use a small proprietary dataset of a few hundred wells, where each well contains a matched combined gas-equivalent production history with separate production histories for the individual condensate and gas products. We do not explicitly consider hydrocarbon composition or fluid properties; rather, we let our machine learning algorithm discern implicit relationships between location, which is directly correlated to fluid properties, hydrocarbon composition, and rock properties, and controllable parameters such as interwell spacing and completions designs. Using a held-out test set, our algorithm accurately captures cumulative volumes of each product over its first two years of production and captures condensate-to-gas ratios (CGR) over the same time span. This method reduces mean absolute percent errors in CGR at IP720 by 10-20% when compared with the traditional approach of estimating in-place CGR in conjunction with a simple decline curve, and accurately predicts decline curve shapes of the CGR history without any human bias. We apply our method to separate datasets from the Montney and Duvernay plays. While different features appear to be more important between the plays, the method offers comparable accuracy in both plays. We ultimately reduce our feature dataset to the publicly reported total gas-equivalent production history, digitized maps of relevant geological properties, and spacing and completions parameters. This feature dataset is all that is necessary to reproduce proprietary production histories of separated condensate and gas streams with a mean absolute percent error in the first two years of less than 30% on average.

For a copy of the paper, reach out below!

Enter your name and email below. We will email the technical paper to you right away.

First name(Required)

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

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