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Fracture diagnostic on a cluster scale of multi-stage hydraulic fracturing wells remains challenging but essential to determine the quality of the stimulation operation and the completion strategies for future wells. Since the stimulation fluid is injected at a different temperature compared to the original geothermal, the considerably modified and highly heterogeneous thermal profile after stimulation presents significant potential to serve for fracture diagnostic purposes. In this work, a model to analyze the temperature signal associated with the shut-in period after hydraulic fracturing is presented, along with the pilot testing of two datasets. The model extends the scope of traditional thermal injection profiling algorithm with fracture diagnostic functions. During the development process, we incorporate the existing warmback model of conventional wells in analyzing shut-in temperature data with a newly developed stimulated region thermal model. Two main outputs of the model, the injection fluid intake and the fracture propagation extent, are estimated and tested. The model is then automated and thoroughly implemented in the software package. The primary applications of this work are injection fluid intake and fracture propagation extent of each perforation cluster in fractured wells. The spatial resolution of the injection profiling and fracture growth can reach the sub-meter scale (same as the distributed temperature sensing spatial resolution). Compared to the conventional radial warmback model, the temperature signals from the fractured well show a much faster warming trend while taking relatively larger amounts of injection fluid. This behavior can be attributed to the additional heat loss to the unstimulated region and larger contact area between clusters. On the other hand, leak-off fluids create a cooler stimulated region around the fracture plane, which makes the warmback trend slower compared to the linear flow regime model. The model developed in this study considers both behaviors to simulate the actual datasets. The inverse model estimates the fracture propagation extent in both the stimulated region as well as the fracture plane. Both estimations can jointly infer the leak-off extent of an individual cluster. As a pilot project, this model is tested on warmback temperature data from two datasets. The injection profiling results using the model are consistent with profiles obtained from other data sources, while the estimated fracture propagation extents of individual clusters present different types of fracture geometry (symmetrical, asymmetrical, double peaks, etc.). Quantitative injection profiling and fracture propagation extent estimations of an individual cluster using warmback analysis have been proven viable and reliable in this field study. It could be the first quantitative warmback analysis applied to fracture wells in the industry.
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.
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