Case History: Real-Time Fiber-Optic Technology Maximizes Tight Carbonate Formation Returns in Kuwait, Multistage Acid Fracturing Diagnostics, Post-Treatment Flowback Allocation, and Production Profiling

Gorgi, Sam; Joya, Jose Francisco; Al-Ebrahim, Ahmed; Al-Othman, Mohammad; Al-Dousari, Mohamad Abdullah; Ahmed, Abdulsamad Mohamad; Hassan, Mohamad Omar; Al-Mansour, Jassim Mohammad; Elsayed, Abdou; Alboueshi, Alaa Eldin; Abbou, Ahmed; Robles, Fernando

doi:10.2523/19178-ms

Cited by 1 publication

(1 citation statement)

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“…It can be used to evaluate fluid distribution across several clusters during fracturing stimulations and to monitor inflow distribution along the horizontal producer using absolute temperature data [33][34][35] and to track the distribution of inflows along with the horizontal producer [36][37][38]. DTS has also been used in assessing production from fractured wells during production [39]. Holley et al [40] described the specific application of DTS in open hole packer completion for a tight gas reservoir.…”

Section: Literature Reviewmentioning

confidence: 99%

Advancement of Hydraulic Fracture Diagnostics in Unconventional Formations

et al. 2021

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Multistage hydraulic fracturing is a technique to extract hydrocarbon from tight and unconventional reservoirs. Although big advancements occurred in this field, understanding of the created fractures location, size, complexity, and proppant distribution is in its infancy. This study provides the recent advances in the methods and techniques used to diagnose hydraulic fractures in unconventional formations. These techniques include tracer flowback analysis, fiber optics such as distributed temperature sensing (DTS) and distributed acoustic sensing (DAS), tiltmeters, microseismic monitoring, and diagnostic fracture injection tests (DFIT). These techniques are used to estimate the fracture length, height, width, complexity, azimuth, cluster efficiency, fracture spacing between laterals, and proppant distribution. Each technique has its advantages and limitations, while integrating more than one technique in fracture diagnostics might result in synergies, leading to a more informative fracture description. DFIT analysis is critical and subjected to the interpreter’s understanding of the process and the formation properties. Hence, the applications of machine learning in fracture diagnostics and DFIT analysis were discussed. The current study presents an extensive review and comparison between different multistage fracture diagnostic methods, and their applicability is provided. The advantages and the limitations of each technique were highlighted, and the possible areas of future research were suggested.

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