This paper presents a knowledge-based system for designing matrix stimulation operations. The two main points in carrying out such a design are to define the technical/economic objectives to be reached and the injection parameters that optimize stimulation. The system takes into consideration these points and generates a matrix stimulation program that includes treatment, spacer and displacement fluids, operating procedures, equipment, reference injection parameters and, finally, diagrams to evaluate the removal of damage during the execution of the job. Expert knowledge has been formalized and implemented using a built-in-house classification shell. The system, preliminarily validated at company headquarters, has been delivered to the operating districts where it is routinely used. The system is part of a more comprehensive project, called PROGRESS (PRoduction Optimization inteGRated Expert SystemS), aimed at providing a decision support system for well production optimization. Introduction The goal of a matrix stimulation job is to minimize the effect of formation damage on the productivity of wells, taking into account technical and economical considerations. A matrix stimulation job consists in injecting a chemicals mixture into the formation. The selection of appropriate chemicals and the design of accurate operating procedures for the job are tasks that only a few experienced technicians are able to carry out. Designing a matrix stimulation job is not only a difficult task, but also a delicate one: besides its cost, a wrong matrix stimulation job (i.e. a stimulation job that fails to remove the preexisting damage) could make the damage worse and possibly irreparable. The effort of developing a matrix stimulation support system is mainly aimed at reducing this risk to a minimum. According to previous considerations, the Matrix Stimulation Design Expert System is used:as a support tool in operating districts when experts are not available. This tool reaches the following objectives:–supports the diffusion of techniques and gives operating personnel more technical autonomy;–encourages standardization in matrix stimulation design by distributing the techniques most recently developed by specialists;–allows experts to dedicate more time to studying new techniques by reducing their involvement in routine problems;as a framework where strategic know-how can be stored;as a training tool for new personnel. The system presented in this paper is one of the four integrated systems of the PROGRESS project (fig. 1) aimed at providing a support tool for well production optimization. The Well Problem Analysis Expert System supports the user in verifying the existence of a production problem and in identifying its possible causes. If formation damage is thought to be one of the causes of well problems, the Formation Damage Expert System helps the user to diagnose the types of damage mechanisms. Once these mechanisms have been diagnosed, the Risk Analysis Expert System performs an a priori evaluation of the economical, technical and logistic risks associated to a matrix stimulation operation. Finally, when the total risk of the matrix stimulation job is thought to be low, the Matrix Stimulation Design Expert System generates an appropriate job program. The architecture of the PROGRESS project has been designed to allow the user to interact with the systems following the above flow or by individually consulting each system. P. 109^

The paper presents an innovative approach to the development of a knowledge-based system aimed at support matrix stimulation design. The basic role played by preliminary context evaluation is enhanced. Context evaluation Involves assessing whether or not the stimulation must be carried out, through technical-economical feasibility analysis and risk assessment. A conceptual model and a system architecture have been specifically defined for this domain. allowing fast and expressive system prototyping. Extensive exemplifications of system reasoning performances are provided. performances are provided. Introduction The large internal demand for matrix stimulation jobs compared to the small number of expert available. the failures caused by lack of skill and inadequate operation control have led to the decision to develop a tool devoted to the broad support of matrix stimulation activity. The target objectives of such a tool are as follows: 1 - outline of detailed procedures for matrix stimulation planning, for selection of chemicals, equipment and service companies and for supplier and service quality control; 2 - data analysis and post-job evaluation; 3 - support for less-experienced users to avoid wasteful and uneconomical matrix stimulations and to design at expert level those which are necessary and cost effective. Knowledge-based systems (KBS) offer a solution to reach those goals. They are well-suited for domains of application where expertise plays a fundamental role In achieving optimal results. Therefore the huge amount of expertise involved in matrix stimulation design makes it the typical kind of domain where this computer technology could be successfully applied. Of the few KBS that have been previously developed in this field, all have highlighted the complexity of the domain. Cram et al. (1986) first Investigated the possibility of using KBS for matrix treatment design. They focused mainly on the selection of optimal chemicals, disregarding the process design. Alegre et al. (1988) dealt with formation damage diagnosis by defining classes of causes, mechanisms and related types of damage. Jia-li Ge et al. (1989) similarly approached damage diagnosis by focusing mainly on handling data uncertainty, using fuzzy logic. In this paper we present MAST (MAtrix Stimulation Treatment), a knowledge-based system developed for improved damage diagnosis and matrix treatment design MAST not only helps with correct damage diagnosis and supports the selection of chemical types and quantities, but it also evaluates economical opportunities and plans the appropriate operations sequence. We recognize that the availability of a domain expert is a crucial point in making a KBS fully reliable. Therefore MAST capabilities have been enhanced by the adoption of a design and evaluation method developed in-house: the key factors increasing the success ratio of matrix stimulations jobs have been identified extensive statistical analysis on more than 650 matrix stimulation jobs. carried out over the past 11 years in 9 countries, allowed us: to understand the reasons for past 11 years in 9 countries, allowed us: to understand the reasons for failures, to obtain a full scale lab (i.e. field) response, to compare lab-test Information with field Indications, and to identify and validate specific techniques aimed at optimizing matrix stimulation design and performance. performance. Currently MAST is the prototype stage and it covers only damage diagnosis. The validation phase is underway and. at the present time, the system has been successfully tested on a present time, the system has been successfully tested on a significant set of field cases. The system is implemented using a commercial knowledge engineering environment, based on an object-oriented paradigm and Common-Lisp programming language. The user interacts with the system using a mouse and programming language. The user interacts with the system using a mouse and a window-based interface. 2 PROBLEM SOLVING IN MATRIX STIMULATION The MAST architecture, shown in fig. 1, reflects the complex line of reasoning used by the expert for optimum matrix stimulation design The expert considers the following groups of Input data: 1 - well objective (i.e. production and/or recovery optimization, economical production rate, etc.); 2 - well data (i.e. bottom-hole flowing pressure. bottom hole temperature, net pay thickness, etc.); 3 - field and history data (i.e. reservoir lithology, mineral contents, static pressure, permeability. porosity, previous treatments. production history); 4 - laboratory results (i.e. acid sensitivity, wettability, formation packing, formation cementation, etc.).

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This paper presents a knowledge-based system (KBS) for the identification of problems in producing wells. The KBS is based on a general Well Problems Analysis (WPA) methodology that entails three main activities: verification of a problem's existence, identification of a preliminary set of possible problems, refinement of the most plausible problem(s). The relevant experts' knowledge has been formalized by means of a classification approach based on the organization of all problems in a hierarchy of classes and, by associating the corresponding symptomatology with them. The system, validated at the company headquarters, is currently in use in the operating districts. The system is a part of a more comprehensive project, called PROGRESS (pRoduction Optimization inteGRated Expert SystemS), aimed at providing a decision support system for well production optimization.