Study of key parameters of reservoir viz, porosity, water saturation, permeability and pore size distribution from well logging data is more complicated in carbonate reservoir due to geological heterogeneities than Clastic reservoir.The Magnolia field is located in GOM blocks GB 783 and 784 and produces from Plio-Pleistocene turbiditic sands that form a complex channel/levee sequence penetrated by 16 boreholes. The primary pays consist of two sands, each about 200 feet thick, separated by a 15 foot shale layer. The pays are divided into an eastern gas prone province and a western oil prone province. A reservoir flow simulation model is planned to optimize production from existing wells and to facilitate future field development. Construction of an accurate model is complicated by MDT pressure measurements which indicated compartmentalization below the resolution of conventional seismic analysis, and by overlap of the seismic attributes derived from producing reservoirs, wet sands, and shales.To mitigate these factors, geostatistical inversion was chosen to produce the rock property inputs for the flow simulation models. This approach allowed development of a rock properties model consistent with core data, log data, and geologic constraints as well as seismic information. It also allowed assessment of uncertainty through the generation of a statistically significant number of internally consistent alternate solutions (realizations). A Markov Chain Monte Carlo method was employed to integrate borehole and geologic information to produce acoustic impedance and lithology volumes which were then used to co-simulate porosity, permeability, p-wave velocity, and water saturation volumes. Multiple realizations of these products were reviewed, uncertainty was assessed, and a rock properties model was selected for conversion to a flow simulation modeling format. The entire process can be rerun relatively quickly to accommodate additional wells and improved seismic data or to match production history.
Deepwater reservoirs pose significant challenges worldwide to companies exploring and producing such reservoirs because of the high exploration, development, and production costs.Great uncertainty and risk attend the evaluation of deepwater reservoirs because of the environment, sparse well control, and lack of direct measurement of reservoir properties. Proper modeling of deepwater reservoirs provides companies with tools to evaluate these reservoirs and quantify risks associated with their development. There are five critical areas in the process of modeling deepwater reservoirs. These are geological and geophysical modeling, reservoir characterization, reservoir flow modeling, facilities/flow assurance, and uncertainty/risk analyses. This paper presents methodologies found useful by experience in the modeling of deepwater reservoirs. Actual field cases describe our experience in using systematic steps based on the five critical areas to model the Zia reservoir, a Manatee reservoir in the Troika Basin, and the Magnolia reservoirs using the Markov Chain Monte Carlo technique. Introduction In this paper, we describe a general modeling process that improves reservoir understanding and performance forecasting.These factors are extremely important in the high cost, high risk deepwater provinces, where wrong decisions lead to expensive mistakes and can materially affect a company's financial standing.In addition to the geological, geophysical, and engineering analyses usually conducted for offshore fields, total systems analysis and risk analysis are critical to assess the economic viability of a deepwater project. Several descriptions of reservoir modeling exist in the literature.Deepwater reservoirs have been subjects of several studies.The integration of geological and geophysical data using geostatistical methods has been discussed in several papers [1–10].The sparse data available makes this step very important in deepwater reservoirs.This integration leads to better reservoir characterization by utilizing a comprehensive data set with low vertical resolution (the seismic data) conditioned to a limited data set with high vertical resolution (well data). Reservoir flow modeling involves upscaling from geocellular models with very large numbers of cells (in most cases) and use of fast software platforms to edit those models when new data becomes available.In addition, these models should be tied to systems models which include wellbore and surface facilities models to properly forecast the achievable rates and expected recoveries from deepwater reservoirs.Several authors have discussed both the reservoir model itself [2–4,12,14] or the reservoir model with integrated facilities [5,9,15–17].Flow assurance studies are crucial to the systems modeling effort as well, and flowline deposits can cause production interruptions and expensive interventions.Although not addressed in any detail in our paper, we note that this step is vital to a successful project. Inclusion of uncertainty can also be time-consuming for flow simulations, so techniques to reduce the time required to investigate the various interaction of reservoir variables as they relate to the uncertainty of the results are very important.Experimental Design (ED) or Design of Experiment (DOE) methods have been found to be very efficient for determining ranges of uncertainty of the results of flow simulations.Friedmann et al.[11], Corbishley et al.[12], and Portella et al.[13] describe approaches using experimental design to reduce the large flow simulation workload. Other uncertainty reduction involves risk analysis to mitigate the risk inherent in development of extremely expensive wells and facilities in the deepwater arena.All of the experimental design discussions involved estimating risk profiles, as did Ghorayeb et al.[15], Capeleiro Pinto et al.[18], and Ring et al.[19]. Understanding the risk profile of any project leads to better decisions for deepwater projects.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.