Portal de Periódicos da CAPES

Fractured Reservoir Characterization and Performance Forecasting Using Geomechanics and Artificial Intelligence

1995; Linguagem: Inglês

10.2523/30572-ms

A. Ouenes, S. Richardson, W.W. Weiss,

Drilling and Well Engineering

Fractured Reservoir Characterization and Performance Forecasting Using Geomechanics and Artificial Intelligence A. Ouenes; A. Ouenes New Mexico Petroleum Recovery Research Center Search for other works by this author on: This Site Google Scholar S. Richardson; S. Richardson New Mexico Petroleum Recovery Research Center Search for other works by this author on: This Site Google Scholar W.W. Weiss W.W. Weiss New Mexico Petroleum Recovery Research Center Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 1995. Paper Number: SPE-30572-MS https://doi.org/10.2118/30572-MS Published: October 22 1995 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Ouenes, A., Richardson, S., and W.W. Weiss. "Fractured Reservoir Characterization and Performance Forecasting Using Geomechanics and Artificial Intelligence." Paper presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 1995. doi: https://doi.org/10.2118/30572-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractA new approach in fractured reservoir characterization and simulation that integrates geomechanics, geology, and reservoir engineering is proposed and illustrated with actual oil reservoirs. This approach uses a neural network to find the relationship between, reservoir structure, bed thickness and the well performance used as an indicator of fracture intensity. Once the relation established, the neural network can be used to forecast primary production, or for mapping the reservoir fracture intensity. The resulting fracture intensity distribution can be used to represent the subsurface fracture network. Using the fracture intensity map and fracture network, directional fracture permeabilities and fracture pore volume can be estimated via a history matching process where only two parameters are adjusted.IntroductionConventional reservoir simulation has benefited from important research during the last few years. The use of geostatistics is slowly moving from the production of grayscale maps" with dubious value and multi-million cell realizations with little practical value to useful input data for reservoir simulators. Although there is still much to be done before these geostatistical realizations will be able to reproduce the past performance of a reservoir, the recent trend shows clearly that major advances have been made in conventional reservoir description. On the other hand, naturally fractured reservoir (NFR) characterization has not enjoyed a similar benefit from any major research effort. Until this work, there is no quantitative methodology to "fill the NFR simulator gridblocks".Most of the current fractured reservoir characterization rely on a qualitative description of the fractures. This is achieved by using mainly structure properties, seismic velocity anisotropy observed with shear or S-waves, and more recently compression or P-waves. However, a reservoir engineer struggling to numerically simulate a fractured reservoir needs more than just the location of "sweet spots."The objective of this paper is to provide a reservoir description methodology that leads to a computer input file for a fractured reservoir simulator which can be used for performance forecasting. This methodology relies on the use of geomechanical concepts derived from reservoir structure and artificial intelligence (AI) tools.Neural networksDuring the past few years, the petroleum industry enthusiastically supported the concept of "integrated systems." Integration of everything is everywhere. From a reservoir engineering point of view, the concept of integration is a necessity not fashion. The necessity exists because of the scarcity of reservoir information and the wide range of scales over which this information is measured. Therefore, a reliable reservoir description must somehow integrate all the existing information at all the scales. The application of stochastic global optimization methods, e.g. simulated annealing, in reservoir description provided new tools for achieving a certain level of integration. However, stochastic global optimization methods were developed in an artificial intelligence context and are more than just simple mathematical optimization methods, as believed by some users. Within the artificial intelligence framework, other tools exist and can be used to integrate various information into a complex reservoir model. The most practical of these integration tools can be found in neurocomputing.There are various ways of looking at a neural network. The most common application is a pattern recognition tool where from a given amount of known information, a neural network is able to be trained to recognize some patterns. In this case, the output of the neural network is very often a binary variable where a value 0 means NO, and a value 1 means YES.P. 425 Keywords: artificial intelligence, fracture intensity map, history, permeability, architecture, intensity, fracture characterization, fractured reservoir, gridblock, spe 30572 Subjects: Reservoir Characterization, Unconventional and Complex Reservoirs, Faults and fracture characterization, Naturally-fractured reservoirs This content is only available via PDF. 1995. Society of Petroleum Engineers You can access this article if you purchase or spend a download.