ORCID Profile
0000-0001-9447-0561
Current Organisations
University of Adelaide
,
Fossilytics Engineered Solutions
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Publisher: Elsevier BV
Date: 09-2022
Publisher: SPE
Date: 14-10-2022
DOI: 10.2118/210757-MS
Abstract: Analyses have been widely applied in production forecasting of oil/gas production in both conventional and unconventional reservoirs. In order to forecast production, traditional regression and machine learning approaches have been applied to various reservoir analysis methods. Nevertheless, these methods are still suboptimal in detecting similar production trends in different wells due to data artifacts (noise, data scatter, outliers) that obscure the reservoir signal and leading to large forecast error, or fail due to lack of data access (inadequate SCADA systems, missing or abhorrent data, and much more). Furthermore, without proper and complete integration into a data system, discipline silos still exist reducing the efficiency of automation. This paper describes a recent field trial conducted in Australia's Cooper Basin with the objective to develop a completely automated end-to-end system in which data are captured directly from the field/SCADA system, automatically imported rocessed, and finally analyzed entirely in automated system using modern computing languages, modern devices incl. IoT, as well as advanced data science and machine learning methods. This was a multidisciplinary undertaking requiring expertise from petroleum, computing rogramming, and data science disciplines. The back-end layer was developed using Wolfram's computation engine, run from an independent server in Australia, while the front-end graphical user interface (GUI) was developed using a combination of Wolfram Language, Java, and JavaScript – all later switched to a Python-React combination after extensive testing. The system was designed to simultaneously capture data real-time from SCADA Historians, IIoT devices, and remote databases for automatic processing and analysis through API's. Automatic processing included "Smart Filtering" using apparent Productivity Index and similar methods. Automated analysis, including scenario analysis, was performed using customized M/L and statistical methods which are then applied to Decline curve analysis (DCA), flowing material balance analysis (FMB), and Water-Oil-Ratio (WOR). The entire procedure is automated, without need for any human intervention.
Publisher: SPE
Date: 13-03-2023
DOI: 10.2118/214146-MS
Abstract: Analyses have been widely applied in production forecasting of oil and gas production in both conventional and unconventional reservoirs. In order to forecast production, to estimate reservoir properties, or to evaluate resources, various statistical and machine learning approaches have been applied to various reservoir analysis methods. Nevertheless, many of these methods are suboptimal in detecting production trends in different wells due to data artifacts (noise, data scatter and outliers, inadequate SCADA systems, production allocation problems) that obscure unit reservoir signals, production trends, and more leading to large forecast error, or fail due to lack of data access (inadequate SCADA systems, missing or abhorrent data, and production allocation problems). This work outlines a method that is currently being used in a commercial setting which combines advanced analytics and machine learning with a modern cloud architecture, provide rapid, repeatable, unbiased estimates of original hydrocarbon -in-place (OHIP), estimated ultimate recovery (EUR), and remaining recoverable (RR), and even deliverability forecasts - all in the presence of abhorrent data.
Publisher: EAGE Publications BV
Date: 03-09-2018
Publisher: CSIRO Publishing
Date: 2018
DOI: 10.1071/AJ17087
Abstract: Rock typing or sub ision of a reservoir either vertically or laterally is an important task in reservoir characterisation and production prediction. Different depositional environments and diagenetic effects create rocks with different grain size distribution and grain sorting. Rock typing and zonation is usually made by analysing log data and core data (mercury injection capillary pressure and permeability measurement). In this paper, we introduce a new technique (approach) for rock typing using fractal theory in which resistivity logs are the only required data. Since resistivity logs are sensitive to rock texture, in this study, deep conventional resistivity logs are used from eight different wells. Fractal theory is applied to our log data to seek any meaningful relationship between the variability of resistivity logs and complexity of rock fabric. Fractal theory has been previously used in many stochastic processes which have common features on multiple scales. The fractal property of a system is usually characterised by a fractal dimension. Therefore, the fractal dimension of all the resistivity logs is obtained. The results of our case studies in the Cooper Basin of Australia show that the fractal dimension of resistivity logs increases from 1.14 to 1.29 for clean to shaly sand respectively, indicating that the fractal dimension increases with complexity of rock texture. The fractal dimension of resistivity logs is indicative of the complexity of pore fabric, and therefore can be used to define rock types.
Location: China
Location: Iran (Islamic Republic of)
No related grants have been discovered for Roozbeh Koochak.