Data from a wide variety of sources are required for reservoir simulation. Simulation itself produces large quantities of data. Yet, good data management practices for reservoir simulation data are typically neither well-understood nor widely investigated. This paper presents a specific architecture to manage reservoir simulation data, discusses experiences from six years of global use, explains adjustments to support changing workflows and outlines challenges that lie ahead.
The architecture consists of a Database Management System (DBMS) and files in managed file directories, called Reservoir Input Output System (RIOS). All simulation input data and results are maintained by a Data Management System (DMS). The reservoir simulator reads input files written from the DBMS to RIOS and writes results to files in RIOS. DBMS, RIOS and integrated management tools (DMS) make up the data management environment.
The environment has been in use inside ExxonMobil since late 2000 and now supports close to 500 users (85% of reservoir engineers). There are over 30 individual databases containing 2TB of online data and about 6TB of online RIOS data. The environment itself introduces some additional work. Support staff is required for maintenance of databases, RIOS areas and problem resolution. Direct user manipulation of data is not permitted and additional tools are required to access and interpret data.
The environment provides many benefits. While it insures data integrity, security and consistency, it also automatically updates defaults, limits, associations, types, etc. This allows running of older simulations and generation of aggregate statistics and usage audit trails.
The architecture and experiences presented in this paper may be unique in the industry. The DMS was designed, developed and deployed over a ten year period. It is a successful software story and is viewed, along with the simulator, as a key enabling technology for success with reservoir simulation within ExxonMobil.
Introduction
Reservoir simulation is inherently a data-intensive process. It starts with geological models and their properties, and assignment of phase behavior or equation of state data, relative permeability and capillary pressure information and geomechanical data. It requires layout of the surface facility network, subsurface configuration of wells, their attributes, pressure and rate limits and other production and optimization constraints. Very often, production history information, hydraulics tables, completion tables and logic for runtime management of wells and surface facilities are needed. Finally, special cases like thermal and fractured reservoir simulations require their own set of additional data.
During simulation, timestepping information, convergence parameters and well performance data can be logged and analyzed. Results, such as pressures and rates from wells and surface facilities and pressures and saturations from the simulation grid can be monitored and recorded. The state of the simulator can be recorded at specificied intervals to enable restart of a run at a later time.
This results in an abundance of data to analyze, visualize, summarize, report and archive. Over the years, many authors have tried to address one aspect or another of this data management problem and many commercial and proprietary simulators have made allowances to simplify users' work in this area1–3. However, in general, data management has not been a widely investigated aspect of reservoir simulation.
Data management in reservoir simulation enables workflows and collaboration, insures data integrity, security and consistency and expedites access to results. In today's computing environment, data management is an enabler to meet the growing need for reservoir simulation and to make simulation available to a wider audience of professionals, including many kinds of engineers and geoscientists.
