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This paper highlights the first successful application of a field deployment of a high-temperature (HT) downhole shut-in tool (DHSIT) in multistage fracturing completions (MSF) producing retrograde gas condensate and from sour carbonate reservoirs. Many gas operators and service providers have made various attempts in the past to evaluate the long-term benefit of MSF completions while deploying DHSIT devices but have achieved only limited success (Ref. 1 and 2). During such deployments, many challenges and difficulties were faced in the attempt to deploy and retrieve those tools as well as to complete sound data interpretation to successfully identify both reservoir, stimulation, and downhole productivity parameters, and especially when having a combination of both heterogeneous rocks having retrograde gas pressure-volume-temperature (PVT) complexities. Therefore, a robust design of a DHSIT was needed to accurately shut-in the well, hold differential pressure, capture downhole pressure transient data, and thereby identify acid fracture design/conductivity, evaluate total KH, reduce wellbore storage effects, properly evaluate transient pressure effects, and then obtain a better understanding of frac geometry, reservoir parameters, and geologic uncertainties. Several aspects were taken into consideration for overcoming those challenges when preparing the DHSIT tool design including but not limited to proper metallurgy selection, enough gas flow area, impact on well drawdown, tool differential pressure, proper elastomer selection, shut-in time programming, internal completion diameter, and battery operation life and temperature. This paper is based on the first successful deployment and retrieval of the DHSIT in a 4-½" MSF sour carbonate gas well. The trial proved that all design considerations were important and took into consideration all well parameters. This project confirmed that DHSIT devices can successfully withstand the challenges of operating in sour carbonate MSF gas wells as well as minimize operational risk. This successful trial demonstrates the value of utilizing the DHSIT, and confirms more tangible values for wellbore conductivity post stimulation. All this was achieved by the proper metallurgy selection, maximizing gas flow area, minimizing the impact on well drawdown, and reducing well shut-in time and deferred gas production. Proper battery selection and elastomer design also enabled the tool to be operated at temperatures as high as 350 °F. The case study includes the detailed analysis of deployment and retrieval lessons learned, and includes equalization procedures, which added to the complexity of the operation. The paper captures all engineering concepts, tool design, setting packer mechanism, deployment procedures, and tool equalization and retrieval along with data evaluation and interpretation. In addition to lessons learned based on the field trial, various recommendations will be presented to minimize operational risk, optimize shut-in time and maximize data quality and interpretation. Utilizing the lessons learned and the developed procedures presented in this paper will allow for the expansion of this technology to different gas well types and formations as well as standardize use to proper evaluate the value of future MSF completions and stimulation designs.
This paper highlights the first successful application of a field deployment of a high-temperature (HT) downhole shut-in tool (DHSIT) in multistage fracturing completions (MSF) producing retrograde gas condensate and from sour carbonate reservoirs. Many gas operators and service providers have made various attempts in the past to evaluate the long-term benefit of MSF completions while deploying DHSIT devices but have achieved only limited success (Ref. 1 and 2). During such deployments, many challenges and difficulties were faced in the attempt to deploy and retrieve those tools as well as to complete sound data interpretation to successfully identify both reservoir, stimulation, and downhole productivity parameters, and especially when having a combination of both heterogeneous rocks having retrograde gas pressure-volume-temperature (PVT) complexities. Therefore, a robust design of a DHSIT was needed to accurately shut-in the well, hold differential pressure, capture downhole pressure transient data, and thereby identify acid fracture design/conductivity, evaluate total KH, reduce wellbore storage effects, properly evaluate transient pressure effects, and then obtain a better understanding of frac geometry, reservoir parameters, and geologic uncertainties. Several aspects were taken into consideration for overcoming those challenges when preparing the DHSIT tool design including but not limited to proper metallurgy selection, enough gas flow area, impact on well drawdown, tool differential pressure, proper elastomer selection, shut-in time programming, internal completion diameter, and battery operation life and temperature. This paper is based on the first successful deployment and retrieval of the DHSIT in a 4-½" MSF sour carbonate gas well. The trial proved that all design considerations were important and took into consideration all well parameters. This project confirmed that DHSIT devices can successfully withstand the challenges of operating in sour carbonate MSF gas wells as well as minimize operational risk. This successful trial demonstrates the value of utilizing the DHSIT, and confirms more tangible values for wellbore conductivity post stimulation. All this was achieved by the proper metallurgy selection, maximizing gas flow area, minimizing the impact on well drawdown, and reducing well shut-in time and deferred gas production. Proper battery selection and elastomer design also enabled the tool to be operated at temperatures as high as 350 °F. The case study includes the detailed analysis of deployment and retrieval lessons learned, and includes equalization procedures, which added to the complexity of the operation. The paper captures all engineering concepts, tool design, setting packer mechanism, deployment procedures, and tool equalization and retrieval along with data evaluation and interpretation. In addition to lessons learned based on the field trial, various recommendations will be presented to minimize operational risk, optimize shut-in time and maximize data quality and interpretation. Utilizing the lessons learned and the developed procedures presented in this paper will allow for the expansion of this technology to different gas well types and formations as well as standardize use to proper evaluate the value of future MSF completions and stimulation designs.
The petroleum industry has continued to show more interest in the application of artificial intelligence (AI). Most professional gatherings now have sub-themes to highlight AI applications. Similarly, the number of publications featuring AI applications has increased. The industry is facing the challenge of scaling up the applications to practical and impactful levels. Most of the applications end up in technical publications and narrow proofs of concept. For the industry's digital transformation objective to be fully achieved, efforts are required to overcome the current limitations. This paper discusses possible causes of the prevailing challenges and prescribes a number of recommendations to overcome them. The recommendations include ways to handle data shortage and unavailability issues, and how AI projects can be designed to provide more impactful solutions, regenerate missing or incomplete logs, and provide alternative workflows to estimate certain reservoir properties. The results of three successful applications are presented to demonstrate the efficacy of the recommendations. The first application estimates a log of reservoir rock cementation factors from wireline data to overcome the limitation of the conventional approach of using a constant value. The second application used the machine learning methodology to regenerate missing logs possibly due to tool failure or bad hole conditions. The third application provides an alternative approach to estimate reservoir rock grain size to overcome the challenges of the conventional core description. Tips on how these applications can be integrated to create a bigger impact on exploration and production (E&P) workflows are shared. It is hoped that this paper will enrich the current AI implementation strategy and practice. It will also encourage increased synergy and collaborative integration of domain expertise and AI methods to make better impact and achieve the digital transformation of E&P business goals.
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