An enormous volume of data, models and decisions are gathered and generated in each and every integrated study. The ability and opportunity, however, for a multi-disciplinary project team, including management, to easily browse, review, visualize and analyze in-progress and final cross-discipline study results in a fast, responsive and easily comprehendible manner does not readily exist. Likewise, it is challenging to easily visualize the impact (potential gain and / or loss) of study uncertainties and alternative study decisions. It is suggested that the integrated Studies Decision Synergy (SDS) initiative is a major new industry step change innovation that is unparalleled in enabling a level of integrated multidisciplinary study synergy across all technical and management professionals at a pace not achieved by traditional industry means. The SDS initiative is composed of five primary components: (1) An integrated multidisciplinary study process (the Event Solution process approach), (2) A proxy, also known as a response surface, to quickly interrogate all study results, (3) Data storage and data mining, (4) Fast, interactive 2D and 3D plotting and mapping, and finally, (5) A user interface enabling process oriented study practice. This paper presents the evolutionary development (technology and process) and study improvement impact (technical workflows, study decisions and results) of the SDS initiative as an innovation product of the Saudi Aramco Event Solution Study Approach (Elrafie et al., 2007). The paper is supported with sanitized illustrations from completed world-class scaled reservoir studies. Additionally, the paper explores potential SDS applications including: (1) Real-time simulation, and (2) A content rich staff skill set development program. The ultimate objective of the SDS initiative is to facilitate easy access, retrieval and visualization of all integrated reservoir study data and decisions by a wide multidisciplinary technical and management community with a specific focus on critical study results and the relationships that exist between: (1) Study uncertainties (static, dynamic, timing, and financial), and (2) Study decisions (strategic, well and operations tactics) in the achievement and success of the project objective (i.e., off take rate, oil recovery, oil rate plateau extent, NPV, etc.).

The use of key wells to improve formation evaluation programs is well established as this method has been proven to improve petrophysical accuracy. In the key wells, extensive logging, coring and fluid sampling programs provide the data used to develop better predictive models for water saturation, lithology, porosity, and permeability. To be effective, key well programs must obtain representative special core (SCAL) data from all cored facies, but this often requires high-density sampling, which may not be cost effective. This paper presents a strategy to meet technical and business objectives by selecting optimum number of samples required to provide the data needed to develop improved petrophysical models. A case study is provided showing the development of an optimum SCAL sampling strategy for a carbonate reservoir. This example highlights how an optimum sampling program for capillary pressure, electrical properties and relative permeability tests leads to enhanced formation evaluation. The case study demonstrates how effective sampling strategies improve determination of water saturation and permeability. It also shows how relative permeability data from a carbonate reservoir may not be directly usable for reservoir simulation if all rock types are not sampled. To represent all rock types, porosity and permeability were initially used to develop hydraulic units. Next, geologic descriptions, which incorporate thin section petrography, X-ray diffraction, and scanning electron microscopy, were used to characterize the reservoir rock in terms of geological facies. X-ray computerized tomography (CT scanning) was used to determine core plug heterogeneity, especially vugginess and mineralogical changes not visible on inspection. Lastly, mercury porosimetry was used to group core plugs of the same rock type based on similarity in pore size distribution. These composited core plugs were then subjected to relative permeability measurements at reservoir conditions. Using the data from this study, core-calibrated saturation and permeability profiles were developed for all rock types. In addition, an analytical relative permeability model for direct input into flow simulators was developed and validated. This model is easy to use and can be modified quite easily during reservoir simulation by changing some predetermined parameters in the model. This should reduce the time and expense of history matching. The model incorporates the scatter in relative permeability data that is usually encountered in laboratory measurements. Introduction The defined goal of coring and core analysis is to reduce uncertainty in reservoir evaluation by providing data that is representative of the reservoir at in-situ conditions. This goal is met by coring to acquire core samples for laboratory analysis which are representative of the reservoir. The well is cored and logged with the purpose of collecting petrophysical data to achieve strategic objectives that result in more accurate hydrocarbon-in-place calculations and better (smarter) reservoir development. Every coring program has several stakeholders and each has a different objective. Geoscientist might be primarily interested in porosity, permeability, diagenesis, pore and rock types, etc.; reservoir engineers might require heterogeneity, anisotropy, relative permeability, capillary pressure, wettability, etc.; petrophysicists would want formation water resisitivity, lithology, water saturation and parameters for calibration of well logs. Drilling and completion engineers would want rock mechanical properties, stress orientation, formation damage, grain size distribution, etc. For the work described in this paper, we shall concentrate on the desires of the reservoir engineers and petrophysicists. But to achieve those set goals we first fulfilled the deliverables of the geoscientists.

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