Inversion of distributed temperature measurements to interpret the flow profile for a multistage fractured horizontal well in low-permeability gas reservoir

Luo, Hongwen; Li, Haitao; Lu, Yu; Li, Ying; Guo, Zhenhua

doi:10.1016/j.apm.2019.07.047

Cited by 22 publications

(13 citation statements)

References 17 publications

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“…To assure the efficiency of inversion computations, a new inversion model derived from the L–M algorithm is developed to reduce the errors between measured DTS data and simulated DTS profiles. 32 Subsequently, through temperature behavior simulation and temperature profile characterization for two-phase-flow MFHWs, a convenient approach to identify and locate water exits from the measured DTS data is proposed as well, which is essential to the flow rate profile interpretation of a two-phase-flow horizontal well. Then, several synthetic cases are analyzed to illustrate the work procedures of the developed inversion system when it is applied to MFHWs with different inflow water distributions.…”

Section: Introductionmentioning

confidence: 99%

“…Is this study, first, a comprehensive temperature model developed for two-phase flow MFHWs in our previous study is used as a forward model to simulate the temperature behaviors in two-phase flow MFHWs. To assure the efficiency of inversion computations, a new inversion model derived from the L–M algorithm is developed to reduce the errors between measured DTS data and simulated DTS profiles . Subsequently, through temperature behavior simulation and temperature profile characterization for two-phase-flow MFHWs, a convenient approach to identify and locate water exits from the measured DTS data is proposed as well, which is essential to the flow rate profile interpretation of a two-phase-flow horizontal well.…”

Section: Introductionmentioning

confidence: 99%

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Flow Rate Profile Interpretation for a Two-Phase Flow in Multistage Fractured Horizontal Wells by Inversion of DTS Data

Luo

Jiang

et al. 2020

ACS Omega

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The use of distributed temperature sensors (DTSs) has become a common practice in real-time downhole monitoring for horizontal wells in oil/gas reservoirs. However, great challenges still exist in translating measured DTS data to flow rate profiles due to lack of robust inversion approaches, especially for multistage fractured horizontal wells (MFHWs) with a gas–water two-phase flow. In this study, a comprehensive inversion system combined with a temperature prediction model and an inversion model has been developed to interpret flow rate profiles for MFHWs with a two-phase flow by inversing downhole DTS data. The temperature model serves as a forward model to predict temperature behaviors of MFHWs. The inversion model is derived from the Levenberg–Marquardt (L–M) algorithm to eliminate the errors between the measured DTS data and the simulated temperature profile. By simulating the temperature behaviors of two-phase flow MFHWs, it has been found that there exist abnormal decreases in the Δ T / x f ratio (temperature drop/fracture half-length) of the corresponding fractures with the production of water. According to this, two effective methods to diagnose water exit locations for an MFHW are introduced. Two synthetic cases are presented to illustrate the application of the inversion system in detail. Finally, a field application is analyzed and satisfactory inversion results are obtained. The maximum inversion temperature error is less than 0.03 K and the absolute error of the inversed gas production rate is less than 9 m 3 /day. The interpreted inflow rates of each stage are close to the measured data as well, which validates the reliability of the proposed inversion system. The findings of this study provide a promising tool to interpret flow rate profiles for an MFHW with a two-phase flow.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flow Rate Profile Interpretation for a Two-Phase Flow in Multistage Fractured Horizontal Wells by Inversion of DTS Data

Luo

Jiang

et al. 2020

ACS Omega

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“…In recent years, many scholars have put forward some interpretation models for horizontal well production logging interpretation, such as the variable coefficient drift model by Lovick and Angeli and the Vedapuri threelayer flow model of an oil-water mixture (Tan et al, 2019). These also include Peebles and Garber's improved one horizontal well slippage speed calculation model (Li et al, 2018), the Harmthy slippage speed calculation model (Luo et al, 2015), Duckler's (Luo et al, 2020a) liquid gas correction model (Luo et al, 2020b), the Nicolas correction model (Yue et al, 2020), the Choquette correction model (Pang et al, 2020), the Abb deviated model (Xie et al, 2020), the Beggs and Brill model (Wu et al, 2020), the CTE slippage model (Jinghong et al, 2020), and the statistical model prediction split phase flow calculation method. Song Yufeng proposes a data fusion expert knowledge base logging interpretation method (Keles et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

An Interpretation Model for the Production Profile on the Same Angle of a Horizontal Well Trajectory

2020

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An accurate interpretation of the fluid production profile is the essential scientific step required for the dynamic monitoring and management of a reservoir. Currently, an accurate interpretation of conventional logging is generally available for vertical wells. However, high-accuracy interpretation is still a blank for horizontal wells, especially for those wells with low production. Therefore, it is of great importance to develop a new model that can efficiently improve the interpretation accuracy of horizontal wells' production profiles. Practically, it was observed from experiments that, for multiphase flow in a horizontal well, the flow pattern changes significantly with the varied inclination angle along the well trajectory. This is because the flow pattern is critically related to the flow rate and the measured phase flow data (i.e., the production data of oil, gas, and water), which varies with the inclination angle along the horizontal well trajectory. Based on the above observations, a new methodology is proposed to interpret horizontal wells' production profiles with the same inclination angle. A comparison of the measured data and numerically calculated results indicates that the newly established method can significantly improve the interpretation accuracy of horizontal wells' production fluid profile. The interpretation method is, therefore, enriched by production logging for different types of wells. By comparing the experimentally measured data and the calculated results, the newly established method is indicated to be capable of significantly improving the interpretation accuracy of horizontal wells' fluid production profile. The interpretation method is further extended to production logging for different well types. By applying this new methodology, the main water-producing layer can be precisely identified to prevent and control the water burst from horizontal wells as well as the formation of water plugging. Consequently, oil and gas recovery can be effectively enhanced.

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“…There have been numerous application studies using distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) in subsurface monitoring. DTS data have been useful for understanding fluid flow behavior (such as flow rate and active fluid flow zone) and reservoir characteristics owing to the hydrothermal coupling in addition to heat transport monitoring (Bense et al, 2016;Freifeld et al, 2008;Luo et al, 2020;Maldaner et al, 2019;des Tombe et al, 2019). DAS has been intensively developed and used to monitor the surface, subsurface shallow reservoirs, or deep structures (Daley et al, 2013;Jousset et al, 2018;Lellouch et al, 2019;Lindsey et al, 2019Lindsey et al, , 2020Zhu and Stensrud, 2019).…”

Section: Introductionmentioning

confidence: 99%

In situ hydromechanical responses during well drilling recorded by fiber-optic distributed strain sensing

et al. 2020

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Abstract. Drilling fluid infiltration during well drilling may induce pore pressure and strain perturbations in neighbored reservoir formations. In this study, we report that such small strain changes (∼20 µε) have been in situ monitored using fiber-optic distributed strain sensing (DSS) in two observation wells with different distances (approximately 3 and 9 m) from the new drilled wellbore in a shallow water aquifer. The results show the layered pattern of the drilling-induced hydromechanical deformation. The pattern could be indicative of (1) fluid pressure diffusion through each zone with distinct permeabilities or (2) the heterogeneous formation damage caused by the mud filter cakes during the drilling. A coupled hydromechanical model is used to interpret the two possibilities. The DSS method could be deployed in similar applications such as geophysical well testing with fluid injection (or extraction) and in studying reservoir fluid flow behavior with hydromechanical responses. The DSS method would be useful for understanding reservoir pressure communication, determining the zones for fluid productions or injection (e.g., for CO2 storage), and optimizing reservoir management and utilization.

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Inversion of distributed temperature measurements to interpret the flow profile for a multistage fractured horizontal well in low-permeability gas reservoir

Cited by 22 publications

References 17 publications

Flow Rate Profile Interpretation for a Two-Phase Flow in Multistage Fractured Horizontal Wells by Inversion of DTS Data

Flow Rate Profile Interpretation for a Two-Phase Flow in Multistage Fractured Horizontal Wells by Inversion of DTS Data

An Interpretation Model for the Production Profile on the Same Angle of a Horizontal Well Trajectory

In situ hydromechanical responses during well drilling recorded by fiber-optic distributed strain sensing

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