ADVERTISEMENT

Simulation Algorithm Benefits by Connecting Geostatistics With Unsupervised Learning

Fig. 1—SOM mapping from input space to neural grids. (a) The high-dimensional input-data space. (b) The neural network in a one-layer 2D lattice grid; each neuron represents a feature drawn from input-data space. The red square represents a neighborhood region. (c) Activated BMU neighborhood. The red square shows the activated neighborhood region surrounding the BMU, shown as a red circle. The blue circles represent included neighborhood neurons other than the BMU at that iteration. Black circles represent deactivated neurons.

This paper presents a new geostatistics modeling methodology that connects geostatistics and machine-learning methodologies, uses nonlinear topological mapping to reduce the original high-dimensional data space, and uses unsupervised-learning algorithms to bypass problems with supervised-learning algorithms. The algorithm presented is a neural topology-preserving pattern-based geostatistical simulation algorithm that integrates the self-organizing map (SOM) concept and its updated version—growing self-organizing map (GSOM)—with an unsupervised competitive learning structure.

Introduction

In oil and gas reservoir modeling, any model construction faces challenges of limited data to some extent. The heuristic behind all geostatistical techniques is the implicit existence of statistical relationships among available data. “Data” here is a broad term; it could be discrete points, such as porosity or permeability at certain locations, but it also could be training images (TIs), which are used in this work. Using TIs as input data originated with multiple-point geostatistics. The aim was to overcome the limitations of using traditional two-point statistical variograms to describe geological continuity, especially in the case of curvilinear structures, which are quite common in nature, such as in fracture networks and geological fluvial structures.

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 190087, “Unsupervised Statistical Learning With Integrated Pattern-Based Geostatistical Simulation,” by Q. Li and R. Aguilera, SPE, University of Calgary, prepared for the 2018 SPE Western Regional Meeting, Garden Grove, California, USA, 22–27 April. The paper has not been peer reviewed.
...
This article is reserved for SPE members and JPT subscribers.
If you would like to continue reading,
please Sign In, JOIN SPE or Subscribe to JPT

Simulation Algorithm Benefits by Connecting Geostatistics With Unsupervised Learning

01 October 2018

Volume: 70 | Issue: 10

STAY CONNECTED

Don't miss out on the latest technology delivered to your email weekly.  Sign up for the JPT newsletter.  If you are not logged in, you will receive a confirmation email that you will need to click on to confirm you want to receive the newsletter.

 

ADVERTISEMENT

ADVERTISEMENT

ADVERTISEMENT

ADVERTISEMENT

ADVERTISEMENT