4D Seismic With Reservoir Simulation Improves Reservoir Forecasting
Authors: Feder Judy 
Published: February 2019

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 191521, “Quantitative Integration of 4D Seismic with Reservoir Simulation,” by Sarath Pavan Ketineni, SPE, Subhash Kalla, SPE, Shauna Oppert, SPE, and Travis Billiter, SPE, Chevron, prepared for the 2018 SPE Annual Technical Conference and Exhibition, Dallas, 24–26 September. The paper has not been peer reviewed.

Standard history-matching work flows use qualitative 4D seismic observations in reservoir modeling and simulation. However, such work flows lack a robust framework for quantitatively integrating 4D seismic interpretations. Four-dimensional, or time-lapse, seismic interpretations provide valuable interwell saturation and pressure information. Quantitatively integrating these data can help constrain simulation parameters and improve the reliability of production modeling. This paper outlines the value of 4D for reducing uncertainty in the range of history-matched models and improving the production forecast.

Introduction

Reservoir-simulation models play an essential role in generating optimal field-development strategies, but they need to be history-matched before they can be used for reliable forecasting. Traditional history matching of a reservoir involves matching observed production and pressure data at well locations by changing the uncertain parameters in the reservoir model within the acceptable range. The parameters can be classified broadly as static and dynamic. Static parameters include permeability, porosity, and net to gross, among many others. Dynamic parameters may include oil/water contacts, fault transmissibilities, relative permeability curves, and flow pathways. 

Deterministic modeling focuses on a single scenario of the reservoir model, ignoring the effect of uncertainty in the parameters considered. Probabilistic history matching is the process of selecting non unique and multiple history-matched reservoir models by altering the uncertain static and dynamic parameters to obtain a range of possible forecasts. Typically, in probabilistic work flows, objective functions composed of a combination of differences in measured and simulated bottomhole pressure data and individual fluid-phase flow rates are used to identify significant factors or uncertainties. The functions are typically based on surveillance data obtained at well locations and therefore may not be sensitive to key reservoir uncertainty away from wells. A good match with production data for a given reservoir model does not ensure robustness in making performance forecasts.

Reservoir heterogeneities—high-permeability pathways, barriers and baffles, or vertical connections forged from geologic erosion—can significantly affect drainage and swept patterns and well-production forecasts. Especially during early stages of field production, history matching to pressure and fluid-rate production data often lacks information necessary to resolve these critical heterogeneities fully, which may significantly affect production. Even a well-posed probabilistic history-matching approach cannot incorporate the variety and complexities of heterogeneities that are yet to have a strong imprint on future production because of insufficient data away from wells and the sheer number of static and dynamic uncertainties that can affect production.