The New Interference Test: Reservoir Connectivity Information from Downhole Temperature Data

Hutchinson, David Anthony; Kuramshina, Najiya; Sheydayev, Ali; Day, Simon N.J.

doi:10.2523/iptc-11672-ms

Cited by 11 publications

(1 citation statement)

References 3 publications

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“…In deep formations with measurable thermal gradient, in addition to hydraulic response data, temperature measurements can be used to enhance the characterization of aquifer hydraulic properties [ Hutchinson et al ., ; Arnold et al ., ]. For example, in deep hydrocarbon reservoirs and enhanced geothermal systems, temperature variations with depth can be quite significant.…”

Section: Introductionmentioning

confidence: 98%

Inference of permeability heterogeneity from joint inversion of transient flow and temperature data

Zhang

Jafarpour

2014

Water Resources Research

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Characterization of the rock permeability distribution in compartmentalized deep aquifers, enhanced geothermal systems, and hydrocarbon reservoirs is important for predicting the flow and transport behavior in these formations. Reliable prediction of the fluid flow and transport processes can, in turn, lead to effective development of the subsurface energy and environmental resources. In deep formations where thermal gradients are significant, the transient temperature data can provide valuable information about the permeability distribution with depth and about the vertical fluid displacement. This paper examines the importance of temperature data in resolving the distribution of permeability with depth by jointly, and individually, integrating the transient temperature and flow data. We demonstrate that when estimating permeability distributions in deep geothermal reservoirs, incorporating temperature data can increase the resolution of the permeability distribution profile with depth. To illustrate the importance of temperature measurements, we adopt a coupled transient heat and fluid flow as a forward model to predict the heat and fluid transport in a geothermal reservoir and develop an adjoint model for efficient computation of the gradient information for model calibration. We perform a series of numerical experiments for integration of flow and pressure data alone, temperature data alone, and flow and pressure jointly with temperature data. In each case, we apply the maximum A-posteriori (MAP) method and the randomized maximum likelihood (RML) method for inversion and uncertainty quantification. Analysis of the sensitivity of temperature and production data to heterogeneous permeability distributions reveals that the temperature of fluid, even when measured at the surface, is sensitive to the permeability distribution in the vertical extent of the reservoir. Hence, temperature measurements can be augmented with flow-related data to enhance the resolution of the estimated permeability field with depth.

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Section: Introductionmentioning

confidence: 98%

Inference of permeability heterogeneity from joint inversion of transient flow and temperature data

Zhang

Jafarpour

2014

Water Resources Research

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The Use of Downhole Temperature Data in Gas-Oil Ratio Estimation and Reservoir Management

Deucher

Gomes

Aniceto

et al. 2011

SPE EUROPEC/EAGE Annual Conference and Exhibition

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In Campos Basin, deepwater offshore Brazil, permanent downhole gauges are used to record pressure and temperature. Pressure data are used extensively in well analysis, but temperature data have seen little use. Production data indicate that downhole temperature changes as a function of flowrate, pressure and fraction of fluids. A strong relationship was observed between flowing temperature and gas-oil ratio (GOR), caused by cooling upon expansion. It has also been observed that temperature data can be used to detect scale occurrence, aiding reservoir management. This paper proposes a methodology based on a semi-empirical equation that makes use of downhole temperature data to estimate GOR. The energy balance applied to the wellbore was simplified to generate an equation suitable to use with easily obtainable field data. The resulting equation has two unknown parameters, which are obtained by adjusting the model to field data. The inputs used to calibrate the model are GOR, oil flowrate, watercut and downhole temperature. The use of this methodology to three horizontal wells will be presented. For well C, the mean error obtained while estimating the GOR measured at the tests was 6.6%. In well B, the mean error was 11%. Well A has passed through a wide range of liquid flowrates, and over the 150 production tests that were performed before water breakthrough, the model was able to estimate the GOR with a mean error of 5.4%. However, after water breakthrough it could not reproduce the measured data accurately. In Campos Basin, accurate GOR measures are difficult to obtain, particularly after gas-lift start-up, because gas flowrate measurement is unreliable and test separators are often unavailable. Using this methodology, it is possible to obtain estimates of the GOR, which is desirable for an improved definition of the waterflood strategy and as a refined input for material balance in specific regions of the reservoir.

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Joint Inversion of Production and Temperature Data for Identification of Permeability Distribution with Depth in Deep Reservoirs

Zhang

Jafarpour

2013

SPE Annual Technical Conference and Exhibition

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Characterization of permeblity variation with depth in compartmentalized deep aquifers, geothermal and hydrocarbon reservoirs is important for prediction of flow and transport in complex subsurface environments and directly affects the development of natural and energy resources. In deep formations, temperature gradient can be significant and temperature data can provide valuable information about fluid displacement and conductivity in the vertical extent of the formation. This paper examines the importance of temperature data in resolving permeability distribution with depth by integrating flow and temperature data jointly and individually. We show that incorporation of temperature data in model calibration of deep aquifers can increase the resolution of permeability distribution profile with depth. To illustrate the importance of temperature measurements, we adopt a coupled heat and fluid flow model as a forward model to predict the heat and fluid transport in an a deep reservoir and perform a series of numerical experiments for integration of flow data alone, temperature data alone, and flow and temperature data jointly. For model calibration, we use the Maximum A-Posteriori (MAP) estimation approach and for uncertainty quantification we apply the Randomized Maximum Likelihood (RML). We develop an adjoint model for the coupled fluid and heat flow system of equations to compute the required gradients for model calibration. Investigation of the sensitivity of temperature and production data to the distribution of permeability shows that while fluid flow rate data can primarily resolve the distribution of permeability in the lateral extent of the reservoir, the fluid temperature data, even when measured at the surface, show sensitivity to permeability variability with depth, allowing for a higher resolution profiling of the permeability map. The results elucidate the value of temperature data in enhancing the resolution of the estimated aquifer permeability maps with depth.

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The New Interference Test: Reservoir Connectivity Information from Downhole Temperature Data

Cited by 11 publications

References 3 publications

Inference of permeability heterogeneity from joint inversion of transient flow and temperature data

Inference of permeability heterogeneity from joint inversion of transient flow and temperature data

The Use of Downhole Temperature Data in Gas-Oil Ratio Estimation and Reservoir Management

Joint Inversion of Production and Temperature Data for Identification of Permeability Distribution with Depth in Deep Reservoirs

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