Probabilistic modelling is one of the most frequently used methods in reservoir simulation to manage uncertainties and assess their impact on reservoir behavior/cumulative production. However, depending on the extent of the uncertainty, 100s of scenarios can be generated leaving engineers unable to meaningfully analyze this data. To remedy this an unsupervised machine learning based workflow was developed to identify unique scenarios which was then paired with an integrated dashboard to enable rapid and deep analysis. A case study was done using data from a Shell operated gas field in the North Sea. Data was first mined from 480 history matched scenarios using python; out of which 20 unique clusters were identified through K-Means clustering of pressure and saturation changes with time in each gridblock. This meant that the team had to look only at 20 scenarios instead of 480 to understand the effect of different inputs on pressure and saturation response. For enhanced analysis, an integrated visualisation dashboard was created to visualize pressure and saturation changes, production profiles and connect them back to input parameters The new methodology enabled the team to integrate different aspects of reservoir modelling from static to dynamic to surface constraints on a single dashboard, making it possible to find patterns in large volumes of data which was previously not possible. For example, a cluster was identified which had high water movement; upon inspection of input parameters it was seen that late life recovery was significantly different in this cluster as compared to others. Being able to visualize different properties of multiple scenarios simultaneously at both group and grid level is a very powerful tool that not only generates insights but significantly reduces analysis time and helps in quality checking property modelling and grid behavior. The developed workflow is quite generic in nature, capable of working with various simulators and can be extended to assessing history match quality in Assisted History Matching (AHM) and multi-scenario modelling. Key parameters impacting different scenarios were identified and the team observed 10x reduction in time and significant reduction in manpower requirements through the new approach
Waterflood management in large mature fields is often time intensive due challenges in integrating and analyzing large volumes of data. Maintaining an updated dynamic model may not be practical for day to day decisions and as such data driven analysis becomes the preferred approach. The conventional workflows usually rely on geometry-based allocation factors of injected water and is not readily integrated with other data sources such as injection-production trend correlations, cased hole logs, pressure and production chemistry data. This paper presents a case study of advanced analytics application to a mature waterflood field where available data was rapidly integrated into an integrated visualization dashboard and machine learning was used to identify injector producer connectivity and allocation factors. This study was carried out for a mature waterflood field with over 15 years of water injection history and over 100 active producers. Python programming was used to clean up and integrate various data sources into an integrated visualization dashboard. Following attributes were identified as indications of producer injector connectivity: (a) Correlation in injection water salinity and produced water salinity (b) Correlation in injection rate trends and produced liquid rate trends, (c) Clear jump in producer ESP intake pressure trends as a sign of response from injection. Machine learning was used to cluster producers based on their produced water salinity trends which enabled shortlisting of injectors potentially connected to them. Another machine learning algorithm was then used to estimate connectivity factors between producers and injectors based on their distance and correlation in injection-production trends. The integrated dashboard was used to quality check results against other data sources e.g. trends in ESP intake pressure, PLT and RFT. Also, for each injector, water utilization factor was calculated based on correlation in cumulative water injection vs cumulative oil production in neighboring producers over last 2 years. The study was completed within a tight timeframe of 5 weeks. The results comprised of an injector-producer connectivity map and each injector’s water utilization factor, which formed the basis for re-evaluation of the existing injection water allocation strategy. The injection targets of individual injectors were revised within their operating constraints by prioritizing them based on their connectivity and water utilization factors, aiming at an estimated gain of 5% in reservoir oil rate. This paper demonstrates the potential of advanced analytics in unlocking valuable insights from various overlooked data sources. For example, in waterflood fields with multiple injection water sources, the contrast in their chemical compositions and aquifer water may partially serve as tracers providing useful information on reservoir connectivity. This paper also serves as a practical example of how digitalization can be adopted in subsurface community’s ways of working and hence be part of the ongoing journey of digital transformation in oil and gas industry.
Having less data is challenging as it adds to the uncertainty around our understanding. On the contrary, huge volume of existing data with continuous additions poses challenges to incorporate them into subsurface models. Data analytics involving integration of large volume of geology, production and surveillance data at a single platform, analysis of data and data trends depicting a physical process can be used to come up at technically robust results in a short time. Data driven analysis and forecasting is carried out for a mature steamflood. Firstly, all data including production, injection, pressure, well test, well, static models, petrophysical logs, GIS data are integrated on a single data analytics platform. Trends in data are established and visualized based on the production, injection and geology enabling production performance analysis at various aggregated levels. The established trends are studied to understand the effectiveness of steam injection recovery mechanism, interference and are then forecasted. Data like PLT and static model information earlier analyzed in silos are integrated and now examined together with production data. Due to data integration at a single platform, connecting and visualizing time series data with spatial location to arrive at certain aggregation or cluster is now rapid and credible. Observations and outcomes from data driven analysis correlate well with the recent years of historical data for well groups with mature production and injection history. For well groups with new and upcoming wells, data driven method may get more biased towards existing but sparse performance thus not capturing the full uncertainty in forecast. Data driven analysis method becomes effective when sufficient data is available to infer trends and results that can be validated with alternate estimation or new data. This technique finds wide application in brownfields with huge data that are either not analyzed or integrated with other information to develop a holistic understanding of subsurface uncertainties. Aided with effective visualization, this technique provides an alternative methodology to enable rapid decision making.
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