A real-time production surveillance and optimization system has been developed to integrate available surveillance data with the objective of driving routine production optimization. The system aims to streamline data capture, automate data quality assurance, integrate high and low frequency data to extract maximum value, optimize the design and analysis of commingled well tests, and provide real-time multi-phase well rate estimates for continuous well performance evaluation. A key challenge identified was the need to understand individual well contribution during commingled well tests, as traditional approaches may provide unrepresentative results. Additionally, the well tests are typically infrequent, thus further limiting the reliability of estimated well rates as production system dynamics between well tests are not accounted for. A third challenge recognized was the need for efficient testing procedures in order to minimize deferred production. To address these issues, a fully integrated model of the production system was used, and is driven by a computational algorithm that automatically calibrates the model to real-time sensor data. A new systematic approach was developed to analyze multi-segment commingled well tests simultaneously to improve the accuracy of resulting measurements. Between well tests, a robust regression algorithm is used to continuously adapt and re-calibrate the model when well conditions change. This algorithm can automatically detect sensor bias and apply an appropriate weighting when calibrating the model. In addition, a regularization technique is also used to prevent physically unrealistic changes in the well parameters between infrequent well tests. The technology is currently applied to an offshore deepwater asset and early benefits include a 2% production uplift realized from optimizing gas lift allocation and performing a single well routing change recommended by the technology. Furthermore, more reliable rate allocation to wells has improved the quality of subsurface models used for reservoir management.
In this paper, we discuss the importance of production logging in tight gas reservoirs due to the large number of commingled entries that are typically associated with tight gas wells. We discuss the challenges associated with obtaining high quality production log data in tight gas reservoirs and the subsequent production log interpretation. Along with addressing these challenges, some initial production logging results are presented that highlight the importance of geology. These results include:Most of a well's production comes from a few zones,Some observed production rates are substantially higher than anticipated based on matrix properties, andIn a few cases, sands that can be locally correlated between wells may be associated with anomalously high production rates. Introduction Interest in unconventional resources is increasing in order to meet the world's growing demand for energy. Unconventional gas resources include tight gas, shale gas, and coal bed methane. The National Petroleum Council (Raymond et al. 2007) reported that there are an estimated 4,024 Tcf of global natural gas resource in tight gas reservoirs and 8,225 Tcf in coalbed methane reservoirs. This paper will focus on tight gas, which is often defined as gas in low permeability rock that must be stimulated in order to obtain commercial flow rates. Tight gas reservoirs pose many challenges to both reservoir engineers and geoscientists. Many of these challenges result from the poor reservoir connectivity due to the low permeabilities and the limited size of some of the sand deposits. Some interesting questions for optimal tight gas development include:How is the reservoir depleted? andWhat geologic features impact water and gas productivity? Since many tight gas developments involve stimulating multiple zones and commingling the production in a common wellbore, understanding the wellbore inflow profile is required to answer these questions. (In this paper, zones are defined as perforated and stimulated sandy intervals identified from their gamma ray signature. Zones may consist of one or more closely spaced sands.) Inflow profiles are traditionally obtained via production logs. However, obtaining high quality production log data in many tight gas wells is difficult due to the large number of closely spaced zones, relatively low flow rates, and water-gas slugging. In the next section, we will provide a field overview. After the overview, we will discuss the challenges with production logging in tight gas wells. The following section will provide some initial results that highlight the importance of geology in the Piceance Creek Unit. The last section will summarize the results. Background on the Piceance Field and Development The Piceance Creek Unit (PCU) was formed in 1940 and is located in northwestern Colorado approximately 15 miles northwest of Rifle. Figure 1 shows the location of the PCU (outlined in black). Recent completions have targeted the Mesaverde formation (see Fig. 2). The development of the Mesaverde in the Piceance basin poses many challenges due to its geologic complexity, low permeability, and thick gross interval.
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