A number of new technologies have been developed offering new capabilities for reservoir monitoring. A group at ExxonMobil has been looking into the impact of these measurements on reservoir management. The result has been one vision for the instrumented oil field. Some key points of this vision are presented in this paper. Introduction We define an instrumented oil field as consisting of downhole and surface equipment designed to provide real time information about well and reservoir conditions. Although remote well control is sometimes included, our focus is on measurements, and in particular, permanent downhole measurements. We begin by outlining the business drivers for these measurements followed by how the new technologies have made these measurements feasible. Example applications of these measurements are given for downhole pressure, temperature and flow measurements followed by applications for permanently deployed seismic receivers. Finally, a summary of key points of the ExxonMobil model for the instrumented oil field is presented. ExxonMobil has some experience with downhole sensors. Since 1989, we have installed approximately 300 permanent downhole instruments in 10 different producing areas for numerous applications and environments. Main Business Driver The main business driver for the development of downhole sensor technology is to maximize the recovery of oil and gas. With continuous downhole measurements we can optimize field performance at both the reservoir level and for individual wells. With the information provided from downhole measurements we can also reduce, and possibly eliminate, unnecessary well intervention costs and the associated risk. Information can be obtained in time to make proactive decisions instead of reacting to crises as they occur, and this information is used to plan facilities upgrades and additional wells through the full life cycle of the field. Primary Reservoir Borehole Measurements New technology is making it possible to acquire real-time continuous measurements in the borehole. Fiber optics and other technologies have made it possible to monitor reservoir pressure, temperature, and eventually flow continuously in time, with denser spacing, greater accuracy and resolution. Downhole monitoring can aid reservoir optimization with continuous pressure monitoring. Measurements can be used to check and update the simulation model, and down hole measurements can aid in reservoir definition and description via production well tests. Figure 1 shows a production interference test. In this example, downhole pressure data are available for 80% of the wells. Pressure data are recorded at 1 second sample rate and are accessible by the Internet. The figure on the right is a map of reservoir porosity. The map indicates poor connectivity between well A and well B. A production interference test was run for these wells and the graph on the left shows the pressure measurements from a downhole sensor in well B over the time period after initiating production in Well A. The graph shows a 3.25 psi pressure drop over a 5 day period indicating greater connectivity between A and B then expected from the porosity map. Another important application for downhole monitoring is well optimization. Downhole sensors can provide earlier identification and diagnosis of production problems
The use of drones fo r geophysical data acquisition and artificial intelligence (AI) for geophysical data processing, imaging, and interpretation are active focus areas in current industry and academic applications. Unlocking their cumulative potential in single-focus applications can have a transformative impact, possibly leading to dramatic cost reductions in key use cases and new application areas for enhanced actionable business intelligence. We present field study results from Texas and California that show the potential for imaging pipelines and other subsurface infrastructure by using AI-based methods on high-resolution aboveground magnetic data. The superior resolution and interpretability over conventional geophysical inversion is demonstrated. The method has the potential to provide actionable intelligence in several business-use cases for detecting and characterizing pipelines, crossing zones for multiple pipes, etc. at dramatically reduced costs. The advanced algorithms and workflows used resulted in a 100-fold increase in efficiency and delivered results in two days compared to what could take several months using generally available open-source deep learning AI workflows and software. Future direction of development is to validate against excavation-/drill-bit-/inline-tool-based ground truth and further extend and develop this process to deliver near real-time results. The techniques used are general and can be applied to other geophysical data including seismic, electromagnetic, and gravity at various scales and resolution.
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