Gas trap prediction from 3D seismic and well test data using machine learning

Dmitry Ivlev
1/23/2024
physics.geo-phcs.LG

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

The aim of this work is to create and apply a methodological approach for predicting gas traps from 3D seismic data and gas well testing. The paper formalizes the approach to creating a training dataset by selecting volumes with established gas saturation and filtration properties within the seismic wavefield. The training dataset thus created is used in a process stack of sequential application of data processing methods and ensemble machine learning algorithms. As a result, a cube of calibrated probabilities of belonging of the study space to gas reservoirs was obtained. The high efficiency of this approach is shown on a delayed test sample of three wells (blind wells). The final value of the gas reservoir prediction quality metric f1 score was 0.893846.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Generalization with Reverse-Calibration of Well and Seismic Data Using Machine Learning Methods for Complex Reservoirs Predicting During Early-Stage Geological Exploration Oil Field

85% match2023

Reservoir Prediction by Machine Learning Methods on The Well Data and Seismic Attributes for Complex Coastal Conditions

85% match2023

Enhancing Petrophysical Studies with Machine Learning: A Field Case Study on Permeability Prediction in Heterogeneous Reservoirs

78% match2023

Data-driven models for production forecasting and decision supporting in petroleum reservoirs

78% match2025

Deep Learning Framework for History Matching CO2 Storage with 4D Seismic and Monitoring Well Data

78% match2024

Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes

77% match2025

Cite this paper

@article{ivlev2024trap,
  title  = {Gas trap prediction from 3D seismic and well test data using machine learning},
  author = {Dmitry Ivlev},
  year   = {2024},
  eprint = {2401.12717},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.12717v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2401.12717.svg)](https://codeofpaper.com/paper/2401.12717)

Discussion