Enhancing Reservoir Understanding by Utilizing Business Intelligence Workflows

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.

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A Business Intelligence Approach to Maximize Value of I-Field Data by Managing Data Quality

Al-Ghamdi

Abdulal

2016

All Days

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With the advent of I-filed (Intelligent Field) data and the increasing volume of various data sources, reservoir simulation engineers aim and work on capitalizing on all of those data in addition to the regular monthly averages. Due to the time scale variation and data frequency, a tool is needed to assist engineers to maximize the value from the data sets and at the same time ensure accuracy and representativeness of the simulation input. This paper discusses a workflow that utilizes Business Intelligence capabilities to compare both data sets, the I-field and monthly averages, and visually identify the anomalies. Several issues can be recognized in and between the data sets that are of different sources and timescales, which will create a struggle for simulation engineers to synchronize both data sets and select the reliable data source to be incorporated into the simulation model, especially when they are working on I-fields where they will be represented with two sets of data. The developed workflow capitalizes on Business Intelligence functionalities that will use an equation to compare the data sets against each other and represent the results in charts to graphically identify the discrepancies. Since the average monthly measurements are stored in monthly format and daily measurements, I-field real-time data, are stored in daily format, the workflow will transform the daily measurements format to match the monthly measurements before the comparison. The workflow will assist simulation engineers to QC (Quality Check) the large sets of data automatically and graphically by locating the areas to focus on in the data, which will reduce human errors, the time needed to examine the data sets and the time needed to alter the format of the daily measurements. Simulation engineers need to QC the data sets before they are integrated into the simulation model to enhance the quality of the model, produce accurate results and reduce the time for simulation engineers to manage the data quality. There are many methods to manage data quality; however, Business Intelligence offers a wide range of data acquisition and mining techniques for QCing. This paper will present how these techniques are used to enhance and streamline the process of data QC for the monthly and daily measurements.

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Enhancing Reservoir Understanding by Utilizing Business Intelligence Workflows

Cited by 5 publications

References 7 publications

Geological and dynamic similarity for reservoir state prediction by well connectivity

Geological and dynamic similarity for reservoir state prediction by well connectivity

Enhancing Reservoir Understanding by Utilizing Business Intelligence in History Match Quality Check

A Business Intelligence Approach to Maximize Value of I-Field Data by Managing Data Quality

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