Production Profiling Optimization Enabled by the Development of Distributed Acoustic Sensing Technology

Abstract

Abstract Distributed Fiber Optic Sensing (DFOS) technology is now a well-established technology for a range of downhole applications. This technology provides many benefits, such as the ability to simultaneously perform different measurements (temperature, acoustic, strain etc.) using a single cable, along the entire length of the well. These advantages enable improved reservoir understanding, proactive responses to possible safety issues, reduction of environmental impact, and optimization of operating time. While various DFOS techniques (Distributed Temperature Sensing - DTS, Distributed Acoustic Sensing - DAS) use the similar principle of launching a pulse of light down the fiber and analyzing backscattered light, there are different approaches to raw data processing and leveraging the acquired data in real time to gain maximum benefit from these measurements. One promising downhole application of DFOS is the analysis of distributed sensing data in a producing well that had previously undergone hydraulic fracturing. This analysis provides valuable insights into the well's performance. Although DTS technology is a widely used tool for production rate estimation of a fractured well, it might have some limitations under certain conditions. To overcome these limitations, one approach would be to combine DTS and DAS data. The acoustic and thermal properties of the DAS data can be used for identifying potential production zones that might be missed by DTS analysis alone. This paper presents a case study involving the acquisition of a temperature data with DTS system, which was then analyzed using a probabilistic software package with a sophisticated temperature model for hydraulic fractured wells. To effectively optimize flow rate and production, minimum and maximum rate values for each zone were set according to observations made on the DAS data. In this case study, zones with minimum and maximum flow rates were identified by automatically implemented DAS Frequency Band Energy (FBE) data analysis. The current integration of DAS and DTS data described in the case study is achieved through an interpretation software package, requiring a large volume of DAS phase data to be transferred from the systems to the processing PC for post-processing. To further develop DAS technology and enable data-driven decisions, a Modular Processing Platform (MPP) using virtualization technologies is proposed as an innovative and practical solution for real-time process optimization at the well-site. Technological advances such as containers and increasingly powerful processing edge hardware, have enabled DAS data to be processed by custom algorithms while maintaining measurement integrity. This development offers the advantage of reducing the need for customized post-processing of the data to ensure rapid decision-making at the time of acquisition. The paper demonstrates the effectiveness of DAS containerized approach through examples from other industries and how a similar approach can be applied to the oil and gas sector.