Saudi Aramco Upstream data volumes have been exploding exponentially over the past few years. Reservoir engineers need to quickly analyze huge volumes of multidisciplinary data coming from multiple sources scattered across the upstream business, such as simulation, seismic, corporate database and real time data from intelligent fields. In addition, it is crucial to capture the scattered multidisciplinary experience that Saudi Aramco has across the upstream business. Multidisciplinary professionals are utilizing many expert systems that require specialized experience in their areas. It is very important to break these silos and bridge the gaps to capture all the scattered knowledge and experience in an integrated media to build shared understanding within multidisciplinary teams. Business intelligence provides a platform for guided processes and workflows that help to overcome these challenges. The developed reservoir engineering business intelligence workflows will help engineers to analyze, visualize and report reservoir simulation results, production development scenarios and economics to enhance decision making process. This paper will discuss the Saudi Aramco methodology to develop reservoir engineering business intelligence workflows that utilize advanced data mining, visual analytics, and predictive analytics techniques. In addition, we will review several workflows that improve the process of history matching and prediction by rapidly identifying trends, anomalies, outliers and patterns in the reservoir simulation results.
The technology to process and analyze simulation model outcomes have improved exponentially in the past few years and gave engineers the ability to analyze results of simulation runs efficiently and effectively. Reservoir simulation engineers need to quickly analyze simulation runs based on difference among models calculated data and measured data to determine the quality of the simulation models. With the help of business intelligence tools, engineers are able to do certain quality checks of the model that enhances reservoir fluid flow understanding. History match quality check dashboard provides the required means to perform qualitative and quantitative analysis for simulation runs. The developed reservoir engineering business intelligence tool helps engineers to extract statistical information of the simulation runs to quality check how close the model mimics historical performance. The tool provides means to quantitatively and qualitatively check critical well performance properties that include water cut, pressure, GOR and oil rate against the measured data. Using this tool, engineers will be able to identify wells (or cluster of wells) with issues in those parameters, allowing the engineer to rank simulation runs according to their history match quality. This paper will discuss the algorithms behind the history match quality check dashboard that utilizes advanced data mining and visual analytics. A case study will exemplify identifying problematic wells in the history matched pressure, water cut, and oil production rate for one of Saudi Arabia field.
This paper describes the upstream data standards that Saudi Aramco has recently implemented for Drilling and is in progress to implement them for other areas such as Production and Reservoir Characterization. These standards enable us to make wider use of our monitoring and collaboration centers through a common approach. It also allows us to leverage the evolving WITSML, PRODML and RESQML standards more effectively in our upstream operations.Saudi Aramco relies on many service organizations to deliver the generated data which requires common and standard data classification when exporting data from source A then importing to destination B. Applying a common approach to information access on a global basis has enabled us to streamline our operations and make wider use of emerging analysis, monitoring and collaboration technologies.Real-time technologies have been utilized to capture, monitor and analyze operational data from well sites so that critical decisions can be made in real-time to reduce and eliminate operational and safety problems, thus reducing non-productive time. That includes high-tech rigs, smart wells, monitoring & collaboration tools and real-time data visualization systems.Our aim has been to implement standard upstream information architecture. Our full implementation of WITSML was the vehicle which allowed this to happen in Drilling & Workover operations. We still believe that over the next years with the advancements of technologies there will be more challenges to support open connectivity between different vendors and services supporting the increasing upstream functions.
One critical step towards building accurate reservoir simulation models is to efficiently assess the quality of the model's history match through identifying discrepancies between historical data and simulation model output to assess the history match quality. The role of Business Intelligence in history matching is crucial as it easily helps simulation engineers mine and manipulate data, visualize plots, identify patterns and run statistical algorithms. The History Match Quality Check (HMQC) tool capitalizes on these techniques and provides the required procedures to perform qualitative and quantitative analysis for simulation results in an efficient manner. The developed tool helps engineers graphically identify wells, or cluster of wells, with discrepancies in pressure and water-cut trend, and offer a wide range of flexibility to rank simulation runs according to history match quality. One of the main advantages of the tool is that it can load data from different database sources as it combines both historical data and simulation output in one platform, and guide engineers to perform necessary adjustments to achieve a better history match. The HMQC tool is a dashboard which provides several tabs primarily for the main history match parameters such as first water or breakthrough timing, last and water-cut deviation and pressure evolution. Moreover, it allows engineers to extract statistical information from the simulation runs for quality check purposes and examine how close the model represents the field/reservoir performance. The tool uses advanced statistical algorithms to estimate the simulated properties' trend based on well performance, including first water breakthrough, water-cut and pressure trend against historical data. This paper explains the algorithms used in the HMQC tool that utilizes Business Intelligence data mining and visual analytics to enhance and streamline the history match process.
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