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In a challenging price environment, developing unconventional plays requires a relentless effort at improving operations practices. However, comparing the impact of different completion designs or well management strategies on well performance remains a challenge due to these plays relatively brief production history and lack of long-term field analogs. This study examines various production durations as potential candidates for reliable indicators of well quality. A reservoir modeling approach for unconventional plays is time consuming and may not properly simulate the complex interaction between subsurface properties and operational strategy. Assessment based on early production may be misleading due to well clean–up issue while comparison based on long-term well performance is inappropriate for quick decision making. This is an approach to find an optimum production duration for performance indicator, early enough to be useful and late enough not to be impacted by wells’ clean-ups. The workflow was developed then tested on Utica Shale unconventional play in Ohio, USA, and using data from 676 producing wells. The results show that predictions of midterm performance start to be reliable only near 180 days of cumulative well production. This study used actual daily production data to confirm that IP-30 days and IP-90 days are not strongly correlated to well actual performance in two year range. It also gives validation for the concept of use of 180-days to 360-days performance as more reliable indicators of well quality. The identification of a reliable quality indicator is a critical first component for any subsequent analysis attempting to identify the underlying factor controlling productivity. This study illustrates the danger inherent in the commonly used industry practice of quantifying well quality based on early production. In particular it shows that the first 30 days of production has almost no correlation with longer term production life.
Oil and water production data are regularly measured in oil field operations and vary from well to well and change with time. Theoretical models are often used to establish the production expectation for different recovery processes. A performance surveillance understanding can be developed by comparing the field production data with the production expectation. This comparison generates quantitative or qualitative signals to determine if the producer meets production expectation or if the producer is underperforming and appropriate operational action is required to address the underperformance.
The case study is for the South Belridge diatomite in California. This hydraulically fractured diatomite reservoir is currently under waterflood and steamflood. A methodology is proposed to establish the production expectation from historical production data. For primary depletion, the formation linear and bilinear flow models are applied to producers with vertical hydraulic fractures. For waterflood, an analytical method based on Buckley-Leveret displacement theory is used. Those analytical methods can predict production and provide surveillance signals for producers in the primary and waterflood recovery stages. For steamflood, a semi-quantitative performance surveillance criterion is proposed based on understanding the mechanistic oil banking concept and reservoir simulation results for steamflood and waterflood. With those models representing expected production performance, an integrated flow regime diagram is proposed for the purpose of production surveillance. A performance expectation can be developed for an individual producer. A significant over-performance relative to the expectation normally indicates changes in the recovery mechanism or improvement in sweep efficiency. A significant under-performance usually signifies an operational issue that requires correction in order to optimize the production performance.
In the case study, the surveillance methodology for producers under primary depletion, waterflood, steamflood are demonstrated using historical production data. In addition, water channeling between injectors and producers and its impact on production performance is discussed. Based on this surveillance methodology, some operational actions were proposed and successful results are demonstrated. Examples of forecast for individual producer in primary depletion stage and field scale prediction in waterflood stage are provided. Application indicates that the proposed methodology can serve as a convenient and practical tool for reservoir surveillance and operational optimization.
Bhardwaj, Charu (Cairn, Oil & Gas Vertical of Vedanta Limited) | Godiyal, Preeti (Cairn, Oil & Gas Vertical of Vedanta Limited) | Mathur, Mohit (Cairn, Oil & Gas Vertical of Vedanta Limited) | Ranjan, Amit (Cairn, Oil & Gas Vertical of Vedanta Limited) | Verma, Saurabh K. (Cairn, Oil & Gas Vertical of Vedanta Limited)
The Raageshwari field is situated within the RJ-ON-90/1 Contract Area. Of the seven producers which have been completed, six flow naturally and one is on artificial lift. A network model was created to verify the maximum producing potential of the field as well as identify any bottlenecks in the system. The model could then be used to evaluate de-bottlenecking options before going into full field implementation. The production potential of each well was categorized into three parameters: Reservoir Maximum Production Potential (RMPP) i.e. the maximum theoretical production that the reservoir can deliver at the sand-face, Well Maximum Production Potential (WMPP) i.e. the maximum production that well can deliver from sand-face to choke valve, and Plant Maximum Production Potential (PMPP) i.e. the maximum production that the surface facilities downstream of choke valve can handle.
Erroneous reserve estimates associated with improper application of Arp's relations has led to the development of alternative rate-time models based on empirical considerations. These models can equally provide good fits for only limited duration production data, but they yield significantly different 30-year-estimated ultimate recovery (EUR). In our previous work, we extensively demonstrated the reliability of cumulative-production model to analyze the production performance of fractured dominated tight and shale reservoirs. In this paper, a number of real field examples are used to generate 30-year-EUR estimates using different rate-time models as well as a production-decline model. Results show that some of rate-time models overestimate EUR with more than 150% compared with the cumulative production-decline model which yields correct estimates in most of the cases.