Modeling Deepwater Reservoirs

Abstract

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.

Integration of Rock Properties Crossplot and Petrophysics for Enhancement of Litho-Fluid Discrimination and Compartmentalizationto Reduce Uncertainty and Mitigate Hydrocarbon Exploration Risk. ‘A Case of ‘NICK' Field, Onshore Niger Delta’.

“…'NICK' field is located approximately 4 km to the west of the Izombe field at an elevation of 25m above mean sea level in a drowned river valley affected by seasonal flooding which limits access to the field during the rainy season [2. ], [9], [10]. The Niger Delta encompasses the whole Niger Delta Province and is located on the Gulf of Guinea (Figure 1).…”

“…[2], [9], [10}. The upper Akata Formation, the delta's marine-shale facies, is the main source rock; interbedded marine shale from the lowermost Agbada Formation may have contributed as well [9,10]. The Agbada Formation's sandstone facies is the reservoir rock; however, the higher Akata Formation's turbidite sand is a possible target in deep water offshore and potentially under presently producing intervals onshore.…”

Methods for Upscaling Diverse Rock Permeability Data for Reservoir Characterization and Modeling

“…In the process of using k φ − transform for reservoir characterization, it is generally recommended to use the cloud transform method 8 . For this method, k φ − cross-plot is subdivided into bins.…”

Reservoir Characterization using Petrophysical Evaluation of W-Field, Onshore Niger Delta

“…"Characterizing the reservoir is a process which describes various properties in reservoirs using all the available data to provide reliable reservoir geologic models for accurate prediction of the performance of a reservoir" [1]. Ezekwe and Filler [2] described "reservoir characterization as a process that involves the integration of various qualities and quantities of data in a consistent way to describe the reservoir properties of interest in inter well locations".…”