Accurate individual well production rates are essential to meet corporate production target plans, optimize reservoir performance and make reservoir management decisions that may require well intervention. Short of installing rate measurement devices on each well, a "back allocation" method is generally employed to assign well production rates, using multiplying factors based on well tests conducted the month before. Apart from the inherent errors, based on the assumption that the wells produce at the same rate throughout the month, the process is also not suited for real-time field management that requires production rates to be known much more frequently.This paper describes the implementation of a system that automates the calculation of individual well production rates using real-time pressure data from permanent sensors installed on the wells. The system, based on integrated physical models of the reservoir, well and surface network, has been successfully used to implement crude blend management in a large Saudi Arabian field, producing from three different reservoirs. The paper also describes how the system is used to automate the validation of well test measurements, allowing the engineers to focus their time on problem wells while ensuring that all wells are reviewed. In addition, field models are kept evergreen and can be utilized by different disciplines for production forecasts. Application of the system could result in significant cost savings, due to reduction in the requirements for physical metering of well production. The system also provides unique optimization opportunities, allowing the engineer to determine the optimum settings to maximize production or revenue. Other benefits include, faster resolution of production problems due to early problem detection, focus on exceptions rather than bulk and massive troubleshooting, and zero-latency applicationassisted decision making, all combining to bring real-time field management and optimization to the engineer's desktop.

In fulfillment of the Saudi Aramco Intelligent Field development initiative, more than 15,000 Permanent Measurement Systems (PMS) have been installed and are transmitting real-time data from both surface and subsurface to the corporate database. However, the full benefits of this investment can only be realized if systems and workflows are put in place to transform the massive amounts of data into actionable information to improve field development and performance. This paper presents a holistic approach to the utilization of data from PMS to enable real-time field management and optimization. We show how Saudi Aramco is integrating this data into the current practices and workflows of well testing and reservoir characterization. Examples of real-time well testing workflows are presented.The workflows take advantage of unplanned shut-ins to characterize the well and reservoir under dynamic conditions. The implementation encountered several challenges in the PMS data transmission, availability, sampling, standardization, storage, and retrieval, which are discussed. We also share our experience in resolving change management issues that arose from our attempt to synchronize the activities of various entities involved in the capture, transmission and transformation of PMS data.The paper demonstrates how a major oil and gas company is leveraging high frequency data from Intelligent Fields to enable field management and optimization in real-time. Application of the workflows presented could result in significant cost savings, due to a reduction in the number of planned shut-ins for pressure buildup tests. In addition, the use of long production history data captured by PMS, enables the determination of not only reservoir boundaries and hydrocarbons in place, but also permeability and skin damage, which normally would require well shut-in, and consequently, loss of production from a few days to a few weeks.

This paper demonstrates how intelligent Field (I-Field) capabilities, including the Enterprise Monitoring System (EMS), are being used in real time to monitor the performance of 11 reservoirs across three Saudi Aramco carbonate Fields to meet the required crude blend, and to provide alerts to engineers when certain reservoir engineering requirements or strategies are violated. Using a real Field example, the paper discusses in detail an I-Field workflow used to monitor reservoir performance during a pre-injection period, starting from real time data gathering, validation and mapping, using Permanent Downhole Monitoring Systems (PDHMS) and integrated surface and subsurface modeling along with data mapping packages. Finally, the paper illustrates how integrated real time data and modeling results are used to optimize production and injection strategies for the three Fields for more efficient real time reservoir management. Introduction During the planning phase for developing a new increment in Saudi Arabia (Figure 1), several challenges were identified suggesting the need for putting eight of the project's eleven reservoirs on power water injection to add reservoir energy prior to oil production start-up [1]. Tight flank permeability preventing enough aquifer support from reaching the crest area of the Field was identified to be a challenge for meeting production target of two of the eleven reservoirs. Long reach flank injectors with ± 2-km of reservoir exposure were recommended as the optimum solution to provide adequate injection rates to support producing area in two reservoirs [1]. Presence of a non-permeable tar mat across the flanks of the Field preventing aquifer support from reaching the producing area was identified to be a challenge for meeting production target of four of the eleven reservoirs. The need to drill or reactivate up-dip injectors placed ±30' above the tar-oil contact (TOC) was recommended as the optimum solution to provide adequate injection rates to support the producing area. Optimum well placement above tar was selected based on a massive data gathering project that was initiated to map the tar mat. Several studies were conducted to confirm minimum impact on ultimate recovery of wedge oil [2].

In recent years there has been a significant growth in the number of Permanent Downhole Measurement System (PDHMS) installations in oil and gas fields around the world, as PDHMS prices have been falling steadily and their reliability has also been increasing. However, the full benefits of this investment can only be realized when it is taken from simple surveillance and monitoring to a source for reservoir characterization. This study assesses the systems and workflows that have been put in place to transform the massive amounts of data, including pressure, flow rate and temperature, into actionable information to improve field development and performance. This study presents a dynamic real-time well testing workflow for utilizing data from from intelligent fields. It discusses the applicability of pressure transient analysis using I-Field data from Permanent Downhole Gauges to characterize reservoir and well performance. While seeking to develop an efficient workflow for the utilization of the real-time data for pressure transient analysis, the study also seeks to assess the effects of producing time, flow rate simplification, and data interruptions on the reliability of the analysis methods and results. Actual real-time pressure data from PDHMS, and flow rate data Multi-Phase Flow Meters (MPFM) were used in the analyses, to determine reservoir parameters and evaluate well performance. This study highlights some of the challenges in using real-time data from PDHMS and MPFM. A commercially available software product was used to filter, and manage the real-time data, as well as the modeling and analysis of the test data.

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