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Bibliographic Details
Main Author: Minato, Hiroaki
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2408.11167
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author Minato, Hiroaki
author_facet Minato, Hiroaki
contents This paper presents a Bayesian multilevel modeling approach for estimating well-level oil and gas production capacities across small geographic areas over multiple time periods. Focusing on a basin, which is a geologically and economically distinguishable drilling region, we model the production capacities of its wells grouped by area and time. Regularizing our inferences with priors, we model area-level and time-level variations as well as well-level variations, incorporating lateral length, water usage, and sand usage at each well. The Maidenhead Coordinate System is used to define uniform geographic areas, many of which contain only a small number of wells in a given time period. First, a Bayesian small-area model is built, using data from the Bakken region from February 2012 to June 2024. Then, the model is expanded to contain temporal dynamics in the production capacities. In addition to general time components, water and sand usage intensities are modeled in estimating production capabilities over time. We find the Bayesian multilevel modeling approach provides a flexible and robust framework for modeling and estimating oil and gas production capacities at area and time levels and for informing area-time predictions with uncertainties.
format Preprint
id arxiv_https___arxiv_org_abs_2408_11167
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Multi-time small-area estimation of oil and gas production capacities by Bayesian multilevel modeling
Minato, Hiroaki
Applications
62F15
J.2; J.4
This paper presents a Bayesian multilevel modeling approach for estimating well-level oil and gas production capacities across small geographic areas over multiple time periods. Focusing on a basin, which is a geologically and economically distinguishable drilling region, we model the production capacities of its wells grouped by area and time. Regularizing our inferences with priors, we model area-level and time-level variations as well as well-level variations, incorporating lateral length, water usage, and sand usage at each well. The Maidenhead Coordinate System is used to define uniform geographic areas, many of which contain only a small number of wells in a given time period. First, a Bayesian small-area model is built, using data from the Bakken region from February 2012 to June 2024. Then, the model is expanded to contain temporal dynamics in the production capacities. In addition to general time components, water and sand usage intensities are modeled in estimating production capabilities over time. We find the Bayesian multilevel modeling approach provides a flexible and robust framework for modeling and estimating oil and gas production capacities at area and time levels and for informing area-time predictions with uncertainties.
title Multi-time small-area estimation of oil and gas production capacities by Bayesian multilevel modeling
topic Applications
62F15
J.2; J.4
url https://arxiv.org/abs/2408.11167