Data and Data Hierarchy

Abstract

Summary This paper presents a new approach to reservoir data issues through hierarchy. Analogies between atmospheric systems and reservoirs, particularly nonlinearity and chaos, are noted. The questions of data criticality and inherent paradoxes of data acquisition programs are discussed with examples. Introduction The importance of data in reservoir management is well-recognized. Less emphasized, however, has been an overall hierarchy among different types of reservoir data. This paper focuses on the relationships between factors affecting data. The theme of this discussion is that all data are not of equal value; hence, there is a hierarchy in data acquisition. The purpose of this paper is to put forth a general framework for addressing reservoir data issues in a modem reservoir management environment. The focus is on reservoir engineering aspects of data for black-oil systems. The framework recognizes the inherent characteristics of reservoirs (i.e., uncertainty and specificity). The paper does not propose a universal master plan for data acquisition (we believe this task is unachievable). Instead, it attempts to answer two fundamental questions.What are some paradoxes and uncertainties affecting data issues?Given common oilfield realities (economic, operational, and technical constraints), can we prioritize objectives and outline a means of achieving them through data programs? Literature Survey Weber and Van Geuns proposed a framework for geologic modeling based on a function of reservoir type and heterogeneities. Their modeling technique covers both deterministic and probabilistic approaches, depending on well density. Van De Graaft and Ealey pointed out the hierarchical nature of reservoir heterogeneity pointed out the hierarchical nature of reservoir heterogeneity based on various scales. They noted that reservoir connectivity and permeability heterogeneity are important at large and small scales, respectively. Berruin and Morse investigated the waterflood performance of horizontal, randomly heterogeneous systems for mobility ratios of 0.5 to 5. They showed that cases of systematic ordering of permeable layers or the presence of crossflow barriers had a larger effect on performance than random stratification. Considerations Affecting Reservoir Performance and Data Reservoir Management Approaches and Data. Reservoir management approaches can be classified into three broad categories: preventive maintenance, problem maintenance, and intermediate. preventive maintenance, problem maintenance, and intermediate. Preventive maintenance relies on a continual data acquisition Preventive maintenance relies on a continual data acquisition program consistent with an anticipatory management philosophy. program consistent with an anticipatory management philosophy. Problem maintenance calls for data collection and analysis only as Problem maintenance calls for data collection and analysis only as problems emerge (e.g., premature water breakthrough or declining problems emerge (e.g., premature water breakthrough or declining rates). Intermediate is a hybrid product of the two approaches. Given the economic and engineering risks of a laissez-faire approach (i.e., problem maintenance), the prudent choice for large reservoirs is preventive maintenance. Some of the reasons for preventive maintenance are irreversibility (i.e., reserves can be preventive maintenance are irreversibility (i.e., reserves can be irreversibly lost), nonrecurring conditions (there are windows of opportunity in collecting some critical data, such as PVT samples and in-situ permeability under single-phase conditions), and variability of recovery (recovery depends on reservoir management practices).