Improving the Estimation of the Chance of Success of a 4D Seismic Project Based on Representative Models

Abstract

Reservoir monitoring has constantly used 4D seismic data and has become an important tool for reservoir management. However, from the economic perspective, the information is only valuable if it influences decisions. It is necessary to decide if the acquisition of new information will be useful for field management. Thus, a methodology was developed to estimate the chance of success of a 4D seismic project. The methodology is an iterative process; it quantifies the increase of the project's value through the optimization of the production strategy for several reservoir models. As the number of reservoir scenarios representing the problem can be high, the optimization process can be time consuming. Therefore, the selection of representative models is an important step of the methodology. The present study discusses the impact of the number of representative models on the expected value of information (EVOI) and chance of success results. The chance of success methodology was applied to a synthetic model to validate the results and show its benefits. The selection of the representative models was based on production and economic results from the reservoir scenarios. Five evaluations were performed; each one considers a different number of representative models. The expected value of information and chance of success varies depending on the number of representative models. However, the EVOI and chance of success results obtained stabilize as the number of representative models increases. The methodology estimates the variation of the expected benefits due to the acquisition of 4D seismic data. The results obtained support the decision-making process, making the methodology an important tool for reservoir management. The decision-making process must consider the current uncertain scenario, the engineering and geophysical studies and also the economic aspect. Measuring and, especially, predicting the economic impact of new information is complex. Thus, the chance of success methodology is a tool to assist the decision maker in the evaluation of new 4D seismic projects.

Estimating the chance of success of information acquisition for the Norne benchmark case

“…The proposed CoS methodology is based on the works of Ferreira and Schiozer (2014) and Ferreira et al (2015), comprising the six steps shown in Figure 1.…”

“…This work estimates the CoS of a 4D seismic project before acquiring 4D seismic data (based on the works of Schiozer, 2014 andFerreira et al, 2015), applied to the Norne benchmark model, considering perfect and imperfect information. The objective is to identify which Representative Model (RM) is closest to the real model and assess the increment on the economic return.…”

“…As these analyses assume perfect information, they provide an upper limit for value information estimate. Ferreira et al (2015) showed that the number of selected RMs impacts the analysis. Higher number of RMs leads to more accurate results, but at a certain point the results stabilize.…”

“…In this context, Ferreira and Schiozer (2014) use the term Expected Value of Information (EVoI) when the valuation is made before the data acquisition, and this is the term we use in the text from now on. Ferreira et al (2015) claimed that being a weighted average, the EVoI does not reflect the variation in the increase of the expected revenue due to information acquisition. In order to complement the traditional approach, the authors proposed the Chance of Success (CoS) methodology, a diagnostic tool that gives ranges of possible outcomes for the increase in the difference of the Net Present Value (NPV) with (NPVwi) and without information (NPVwoi) considering different scenarios.…”