Reservoir simulation and modeling based on artificial intelligence and data mining (AI&DM)

In this paper a new class of reservoir models that are developed based on the pattern recognition technologies collectively known as Artificial Intelligence and Data Mining (AI&DM) is introduced. The workflows developed based on this new class of reservoir simulation and modeling tools break new ground in modeling fluid flow through porous media by providing a completely new and different angle on reservoir simulation and modeling. The philosophy behind this modeling approach and its major commonalities and differences with numerical and analytical models are explored and two different categories of such models are explained. Details of this technology are presented using examples of most recent applications to several prolific reservoirs in the Middle East and in the Gulf of Mexico. AI-based Reservoir Models can be developed for green or brown fields. Since these models are developed based on spatio-temporal databases that are specifically developed for this purpose, they require the existence of a basic numerical reservoir simulator for the green fields while can be developed entirely based on historical data for brown fields. The run-time of AI-based Reservoir Models that provide complete field responses is measured in seconds rather than hours and days (even for a multimillion grid block reservoir). Therefore, providing means for fast track reservoir analysis and AI-assisted history matching are intrinsic characteristics of these models. AI-based Reservoir Models can, in some cases, completely substitute numerical reservoir simulation models, work side by side but completely independent or be integrated with them in order to increase their productivity. Advantages associated with AI-based Reservoir Models are short development time, low development cost, fast track analysis and practical capability to quantify the uncertainties associated with the static model. AI-based Reservoir Model includes a novel design tool for comprehensive analysis of the full field and design of field development strategies to meet operational targets. They have open data requirement architecture that can accommodate a wide variety of data from pressure tests to seismic. 2011 Elsevier B.V. All rights reserved. 1. Numerical reservoir simulation and modeling vs. AI-based reservoir modeling Pattern recognition capabilities of Artificial Intelligence & Data Mining (AI&DM) can play many different roles in assisting engineers and geoscientists in building reservoir simulation models. The objective of this article is to introduce a set of comprehensive and complete workflows that have been developed based on the AI&DM for building full field reservoir simulation models. Two of these workflows will be covered in this article. Complete and detail (step by step) procedure for developing these models requires multiple manuscripts (some of which are referenced here) and cannot be included in one paper. Therefore, this article is intended to serve as an introduction to the technology and will be followed by more details manuscripts. In order to put these new AI-based workflows in perspective and for the purposes of this article, let us summarize reservoir simulation and modeling as a process that ultimately models production from a field (of multiple wells) as a function of reservoir and fluid characteristics, operational constraints and other variables in the following formulation: q 1⁄4 f ðx1; x2;.; xn;&y1; y2;.; yn;&w1;w2;.;wnÞ where q is the production from the reservoir; x1, x2,., xn, reservoir and fluid characteristics; y1, y2, ., yn, operational constraints; w1, w2, ., wn, other parameters; f( ), functional relationship. The above equation simply states that production from a field is modeled using a series of functional relationships between E-mail addresses: [email protected], vida_gholami2005@ yahoo.com.