In model based oil field operations, engineers rely on simulations (and hence simulation models) to make important operational decisions on a daily basis. Three problems that are commonly encountered in such operations are: on-demand access to information, integrated view of information, and knowledge management. The first two problems of on-demand access and information integration arise because a number of different kinds of simulation models are created and used. Since these models are created by different processes and people, the same information could be represented differently across models. A unified view of the models and their simulations is desirable for decision making, and thus the necessity for information integration. Knowledge management refers to a systematic way to capture the rationale (knowledge) behind the various analyses performed by an engineer and decisions taken based on the analyses. It is critical to capture this knowledge for auditing, archiving, and training purposes. In this paper, we propose the application of semantic web technologies to address these problems. The key elements of the semantic web approach are the ontologies or the information schemas that model various elements from the domain, and a knowledge base (KB) which is a central repository of the instance information in the system. We present a modular approach for organizing the ontologies and outline the process that was followed to define the ontologies. We also describe the workflow that was used to populate the KB and briefly discuss some of our prototype applications that address the problems mentioned above. Based on our experience, semantic web technologies appear to be a highly promising approach to deal with these information management issues in the oilfield domain, although performance and tool support remain the key areas of concern at this stage.

In the Gulf of Mexico, there has been an increase in the number of wells drilled to depths greater than 20,000 ft with bottomhole pressures exceeding 20,000 psi. These deeper wells present drilling and completion challenges to the industry. Two of these challenges include fracture stimulation for low permeability and frac and pack sand control for higher permeability. Because of the high fracture gradient and friction in the wellbore tubulars, a conventional 1.0 to 1.04 SG fracturing fluid would require surface treating pressures greater than 15,000 psi. In the offshore marine environment, 15,000 psi pressure is the current limit of the flexible treatment line that transmits fluid from the stimulation equipment on the marine vessel to the wellhead on the rig.To solve this limitation, a borate-crosslinked high-density fracturing (HDF) fluid with of up to 1.38 was developed to harnesses the power of gravity and reduce the amount of surface treating pressure required to achieve adequate bottomhole fracturing pressure without exceeding the safety limits of the surface equipment. In numerous wells, a minimum of 20% reduction in surface treating pressure over the conventional 1.04 SG was recorded.This paper summarizes the well conditions, extensive fluid qualification testing, procedures, and selected job results along with final completion performance indicators.The HDF fluid enables treatment of these deep offshore wells by lowering surface treating pressure. Conventional 15,000 psi equipment could be used, less horsepower was required, and creating a safer work environment was achieved.

Design space exploration (DSE) is a common yet complex workflow in oilfield asset development. The "design" of an oilfield refers to a set of decisions about aspects ranging from well locations and number to facility sizing for optimum production. Evaluation of alternate designs -based extensively on reservoir simulations -corresponds to the evaluation of alternate development scenarios in face of uncertainty about subsurface structure and properties. The outputs of DSE influence many decisions in the development phase of an oilfield as well as operational decisions in a producing asset. In this work, we design and implement a generic framework to support DSE workflows in oilfield asset development. Our framework provides tools and services to allow rapid specification and evaluation of multiple design candidates using multiple realizations. The framework also supports hierarchical DSE workflows that allow users to first explore a large design space using proxy models and selectively refine the simulation quality of a smaller subset of designs via fine grained, detailed simulations. The usefulness of this framework is demonstrated through a case study that considers the design problem of selecting a drilling schedule for wells in an offshore oil and gas field. IntroductionDesign space exploration (DSE) refers to the general problem of selecting the values of a set of variables (input parameters) in order to optimize a certain function of those parameters. If the design problem involves many variables, it is typically impossible to exhaustively enumerate and then evaluate all design options in light of constraints on computational resources and the time to make the decision. For such cases, design space exploration can be modeled as a two-phase process: sampling (exploration) of the design space to identify a subset of design points, followed by an evaluation of each design point based on the utility function of interest to the decision maker. Commonly used techniques for the first phase include exhaustive exploration, random sampling, genetic algorithms, simulated annealing, etc. Evaluation of a design point can be abstracted as a function which can range from a simple arithmetic expression to a complex simulation. Because of the large number of design points to be evaluated for a non-trivial design space and the computing resources required for evaluating a given design point, DSE is typically a compute-intensive and data-intensive process.DSE plays an important role in oilfield asset development. For instance, a reservoir development strategy deals with many decision variables, such as parameters in the field production system and physical properties of the geological reservoir. Some variables represent factors that can be controlled, and others represent uncertainty in available information at that stage. These variables are usually used as inputs to a reservoir simulator to generate a forecast of the production profile. To find the best development strategy, a simulation engineer generates design points from ...