A criterion is developed for the selection of the best pairing of the control and manipulated variables of a multiloop control system. This criterion is based on the difficulty caused by the interaction terms (the off-diagonal elements) in finding the inverse of the steady state gain matrix. From an analysis based on the proposition that the most desired or best pairing is that one for which the system most closely resembles a set of independent single-loop systems, a quantitative measure of the best pairing is obtained. Although the development of the pairing criterion is based on purely algebraic principles, the validity of the pairing criterion is evident from analogous developments obtained within the control framework. Furthermore, it is shown that the pairing criterion may be used to determine the stability of a multiloop control system, thus enhancing the value of the criterion presented. SCOPEDespite all the attention given by researchers and theoreticians to interaction analysis and variable pairing for multiloop control systems, it continues to represent one of the most difficult control problems facing engineers today. Because of its calculational simplicity, the relative gain (Bristol, 1966) has been by far the most popular pairing technique. This technique has been applied with some success to high-order systems (3 x 3 or higher). However, for high-order systems, cases have appeared in the literature for which Bristol's pairing rule recommends an unstable pairing (McAvoy, 1983), or fails to predict the interaction of the system (Friedly, 1984). This led to the development of extensions to improve the relative gain, such as that presented by McAvoy, which uses Niederlinski's stability theorem, and by Friedly, which analyzes the ratio of the nondiagonal terms over the diagonal terms of the process gain matrix. Also, a new pairing criterion was proposed by Bruns and Smith (1982), which uses the singular value decomposition (SVD) of the process gain matrix. However, the results obtained by this technique are strongly dependent upon the scaling of the process gain matrix. Jerome (1982) and Jensen et al. (1986) concluded that computer-based methods involving interactive graphics are the most reliable methods for pairing. However, the complexity and hardware requirements of these methods have reduced their use and acceptance.In this paper, a simple, reliable, and scale-invariant steady state pairing criterion is developed. The criterion is developed by an analysis of an iterative technique for obtaining the solution to a set of linear equations, or the inverse of a matrix. It is shown that the system interactions can be assessed by analyzing the effect of the off-diagonal elements of the steady state matrix on the difficulty of obtaining the desired solution or matrix inverse by the iterative process. The rate of convergence of the iterative process for the different variable pairings provides the basis for the pairing criterion. It is demonstrated that the algebraic iterative procedure may be represented ...

The operation of oil and gas fields requires that a multilevel decision hierarchy is used to address field optimization at all timescales. These decisions affect production volumes and costs, not only in their short-term outcomes but also over the life of the field. Depending on the time scale, several models with varying degrees of complexity and detail are typically used to characterize the reservoir (e.g. material balance, fullphysics model, and decline curve). However, the strategy to integrate and maintain these separate reservoir models (describing the same field) is often ad hoc and inconsistent. To make predictions suitable for short-term decisions, proxy-models (e.g. neural networks, response surface) have been proposed. However, these black-box models do not consider the underlying physics of the reservoir phenomena and are limited to the effects captured in the training data set. In this paper, we build upon our earlier work [1] on integration of full-field strategic models (physics-based) and short range operational models based on the moving-horizon, parametric identification approach for reservoir simulation. A short-range, reduced-order model structure is developed, and the model parameters are obtained from production history data. Because the model structure is motivated by the decomposition of a full-physics model, it is expected to be feasible to extrapolate outside the range of history data. The reduced-order model also increases the computational efficiency and effectiveness in carrying out the simulation objectives. The benefit of the proposed model is to assist in the short-term decision making in production operations. This paper provides a discussion of the methodology for identifying such physics-based parametric models for production operation workflows. It also presents case studies to illustrate the benefits of this method for real time production operations and closed-loop reservoir management. Introduction Global production optimization is a multi-time scale problem that requires integration and coordination of several work processes to make decisions on reservoir exploitation strategy, field development and production operations of oil and gas assets. The elements of these work processes span the organization and include several activities at multiple time scales, which pose challenging problems for real time production optimization (RTPO). The industry typically addresses this issue through the adoption of different technologies (for modeling and simulation) across the production supply chain, and through appropriate organization of its personnel. However, all such methods require the description of a reservoir by a model that can predict field production for making optimum decisions. The main purposes of reservoir modeling and simulation are to estimate production and to predict ultimate recovery of reserves under a variety of operating strategies. Such strategies may include various well configurations (e.g. horizontal and multilateral), well locations, well schedules and production rates, and fluid injection mechanisms. The approach to reservoir modeling is often influenced by the knowledge of the underlying phenomena and the purpose of the business work process requiring a model. The following scenarios summarize the different possibilities that arise in reservoir modeling:Determining the effects of measured causes with known causal relationship (e.g. production profiles or reservoir pressure forecasting),.Determining the cause from measured/expected effects with known causal relationship (e.g. production optimization).Determining the relationship between causes and effects, which are both observable and measured (e.g. history matching). The first approach is the conventional forward modeling problem, which is used to predict the evolution of a system output for analytical studies. The latter two approaches are classified as a class of inverse problems, in which the causes (boundary conditions) and the causal relationship (model) are determined from observations of operating data.

Reservoir simulation has become the de facto design and analysis tool to plan, develop, and manage oil and gas assets. With increasing complexity of flow networks and advanced recovery mechanisms in the fields, the model description and features of the reservoir simulator have also been progressively advancing. The goal of a single, evolving, life-cycle model for oil and gas assets has many benefits for effective and efficient field development and exploitation. However, the size and complexity of the reservoir models often require characterization at several resolutions, thus ranging from full field strategic models to short range operational models. Full field strategic models can be used to evaluate various production scenarios and development strategies and to estimate future drilling and facilities requirements. Short range operational models concentrate on issues such as rate requirements, production decline analysis, etc. However, the approach to integrate and maintain these separate reservoir models while describing the same field is often ad hoc and many times, inconsistent. This paper describes a new methodology for enhanced and effective use of reservoir simulation. Specifically, the application of a new method is presented to consistently integrate the full field strategic models and the short range operational models using a parametric system identification approach. The measurements from the field are used to continuously update the short range operational models over a moving time horizon, while simultaneously preparing the data for a history match of the full-field, strategic model. This hierarchical model structure at different scales avoids frequent and costly history-match runs of the larger strategic models without compromising on short term accuracy, for example, those required by production optimization. In addition, the hierarchical model structure improves effectiveness and efficiency in carrying out the simulation objectives. A case study of a full-field performance is presented to highlight the benefits of the method. Introduction The increasing availability of real-time measurements and remotely activated valves in an oilfield has made oilfield-wide optimization of operations a distinct possibility.[1] While the term real-time optimization (RTO) is certainly not new and RTO is practiced in elements of drilling or production operations,[2–4] the extent to which RTO is now feasible has increased dramatically. At the same time, the increased scope of RTO of oilfield operations entails significant complexity and creates challenges. RTO technologies have been advanced, either within the oil and gas industry or in related industries, such as oil refining. While it would certainly be beneficial to further develop technologies for field-wide RTO, it is also useful to identify existing technologies suitable for the task, streamline such technologies for use in the oilfield, and ensure that such technologies are used prudently and ultimately add value Because elements of field-wide RTO can be manifested in many activities related to production optimization, one may be overwhelmed by the multitude of approaches and breadth of scope of field-wide RTO. Putting field-wide RTO in a concrete framework, as discussed in the next section, offers clear development and implementation benefits, in that it can catalyze progress by suggesting the path to long-term benefits which might not be immediately obvious from incremental improvements stemming from individual projects.

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Digital Oil Field (DOF) in the oil and gas industry has gained momentum in the last few years and has transformed from being a vision to projects that have measurable value. The promise of DOF has motivated many oil and gas operators and service companies to now establish corporate initiatives and associated business programs to develop and deploy DOF solutions. Many major capital projects are already evaluating the feasibility of DOF in early stages of the project decision process as part of the operational philosophy.However, the implementation of these projects over the last few years has unraveled a multitude of practical challenges and hurdles to achieve the DOF goals, including selecting the candidate of highest value opportunities in the business unit's portfolio, justifying a business case, establishing new management processes to address operational transformation, defining people roles and responsibilities in DOF work processes, forming a team of skilled personnel for development and support of installed solutions, recognizing fit-for-purpose models, identifying appropriate technologies for the rapid deployment of integrated workflows, lack of open architecture and standards, project management approach, and change management, among others. This paper describes the challenges faced in DOF implementations by Halliburton and the current industry status. The case studies presented highlight these issues and practical lessons learned about addressing these issues using novel solutions and delivering value through adopting best practices. The paper also provides an insight into future trends and areas of development, addressing areas of challenges that still must be resolved. History of Digital Oil FieldThe history of DOF spans several decades, although the current terminologies to describe it have emerged recently. Several terms recently used to describe the digital oil field include smart field, i-field, field of the future, intelligent oilfield, digital asset, e-field, real time optimization, and real time operations. In the early 2000s, operators, service companies, software vendors, and academics have struggled to collectively define DOF. Saputelli, Mochizuki, and Hutchins (2003) provided one of the earliest descriptions of real time optimization (RTO) in this current phase of DOF. More recently, Cramer (2007) described the evolution of DOF from an operating company viewpoint.Early DOF applications began with the digitization of operational data that were previously collected manually. Spreadsheet applications to collect and organize data from different wells in a field and from multiple fields enabled the engineers to perform basic production analyses. Electronic instrumentation at the well sites and SCADA systems enhanced the data capture process with the aid of improvements in telecommunications. These changes enabled access to data that were previously unavailable or at best available through indirect measurements that were prone to errors and inaccuracies. Automatic data capture increased the amoun...