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Recent developments in the deployment of distributed pressure measurement devices in horizontal wells carry the promise to lead to a new, cheap and reliable way of monitoring production and reservoir performance. We theoretically examine the identifiability of reservoir parameters from distributed pressure measurements in the well. The wellbore and near-wellbore are described by semi-analytical steady state models, and a gradient-based inversion method is applied to estimate the permeability of layers that are perpendicular to the wellbore axis. To obtain the derivative information we employ the adjoint method which results in a computationally very efficient inversion scheme. Through several synthetic examples we investigated the effects of well and reservoir parameters, sensor spacing, and measurement noise on the quality of the inversion results. In particular we considered a 2000 m long horizontal well passing through two 300 m long high-permeability streaks in a 10 times lower permeability background. The location of high-permeability zones could be detected with a fair accuracy using 20 unknown parameters (specific PI values) even when the number of measurements was four times less than the number of parameters. Moreover, with 0.01 MPa (1.5 psi) measurement noise (and an average wellbore pressure of approximately 20 MPa (3000 psi)) the estimated specific PI profiles were satisfactory and the high permeability streaks were still detectable. However, when the noise level increased to 0.1 MPa, only the high permeable zone close to the heel was detectable. The negative effects of measurement noise and low sensor/parameter ratios are strongest in those areas of the well where the influx is smallest (usually close to the toe). The inversions typically required less than 90 seconds on a standard laptop. This offers the opportunity to extend the algorithm to multi-phase flow and dynamic applications (pressure-transient testing) while still maintaining sufficient computational speed to perform the inversion online.
Recent developments in the deployment of distributed pressure measurement devices in horizontal wells carry the promise to lead to a new, cheap and reliable way of monitoring production and reservoir performance. We theoretically examine the identifiability of reservoir parameters from distributed pressure measurements in the well. The wellbore and near-wellbore are described by semi-analytical steady state models, and a gradient-based inversion method is applied to estimate the permeability of layers that are perpendicular to the wellbore axis. To obtain the derivative information we employ the adjoint method which results in a computationally very efficient inversion scheme. Through several synthetic examples we investigated the effects of well and reservoir parameters, sensor spacing, and measurement noise on the quality of the inversion results. In particular we considered a 2000 m long horizontal well passing through two 300 m long high-permeability streaks in a 10 times lower permeability background. The location of high-permeability zones could be detected with a fair accuracy using 20 unknown parameters (specific PI values) even when the number of measurements was four times less than the number of parameters. Moreover, with 0.01 MPa (1.5 psi) measurement noise (and an average wellbore pressure of approximately 20 MPa (3000 psi)) the estimated specific PI profiles were satisfactory and the high permeability streaks were still detectable. However, when the noise level increased to 0.1 MPa, only the high permeable zone close to the heel was detectable. The negative effects of measurement noise and low sensor/parameter ratios are strongest in those areas of the well where the influx is smallest (usually close to the toe). The inversions typically required less than 90 seconds on a standard laptop. This offers the opportunity to extend the algorithm to multi-phase flow and dynamic applications (pressure-transient testing) while still maintaining sufficient computational speed to perform the inversion online.
Prior to field-scale development of chemical EOR processes, pilot tests are widely accepted in the oil industry as a standard method to determine the efficiency of the formulated chemicals. During such tests there can be significant differences in temperature between the injected and reservoir fluids. This results in a cool-down of the wellbore, near-wellbore and inter-well regions which can be aggravated in high temperature reservoirs. Key features of surfactant flooding, such as interfacial tension (IFT) reduction between the oil and water phases, depend strongly on temperature. As a result it is necessary to estimate the strength of this cool-down effect upon designing pilot tests. This is the topic of this paper which addresses several scales ranging from near-wellbore to pilot pattern. This work assesses the impact of temperature gradients during a pilot test on the efficiency of surfactant injection using advanced reservoir simulation. We first determine the temperature window seen by an injected surfactant solution with the aim of understanding how it may drive surfactant formulation. We then apply our findings on a pilot design study, with a model including a temperature dependent IFT. We analyse the sensitivity of given injection sequence and operational constraints to specific properties of the injected surfactant solution (low-IFT temperature windows) and then propose a methodology to determine the most efficient injection sequence for a specific surfactant formulation. We show that the temperature window encountered by the surfactant is very sensitive to thermal history of the reservoir and injection temperature. The analysis of chemicals slug thermal and compositional mixing with in-situ fluids is found to be a game changer for reliable pilot design and production forecasts. Obtaining the lowest IFT between oil and water phases is the key in surfactant flooding efficiency: as such the in-situ temperature profiles obtained by simulation and the formulation design at the laboratory should be closely linked. We demonstrate that the process is considerably sensitive to temperature and suggest as a result the following workflow for the design of injection sequences during a pilot test: 1) assessing the temperature window that will be seen by the surfactant using simulation, 2) designing an adequate surfactant formulation, 3) estimating an optimal and robust surfactant injection sequence using simulation, 4) iterating between the three previous steps until an optimal recovery is achieved with a laboratory-formulated, cost-effective surfactant. The impact of temperature on surfactant pilot tests is a specific, not so well documented subject, although it is a capital step in the feasibility assessment of a field scale deployment of surfactant EOR technology. Our workflow yields a reliable assessment of temperature landscapes seen by the injected fluids, which may then be used to test surfactant formulations from near-wellbore to interwell/reservoir scale (e.g. for designing and performing single well chemical tracer tests). As such it should be of interest to petroleum engineers, production engineers and chemists working on the design of chemical EOR processes.
This paper highlights the application of downhole fiber optic (FO) distributed temperature sensing (DTS) measurements for well and reservoir management applications: 1) Wellbore water injectivity profiling. 2) Mapping of injection water movement in an underlying reservoir. The U.A.E. field in question is an elongated anticline containing several stacked carbonate oil bearing reservoirs (Figure 1). Reservoir A, where two DTS monitored, peripheral horizontal water injectors (Y-1 and Y-2) were drilled, is less developed and tighter than the immediately underlying, more prolific Reservoir B with 40 years of oil production and water injection history. Reservoirs A and B are of Lower Cretaceous age, limestone fabrics made up of several 4th order cycles, subdivided by several thin intra dense, 2-5 ft thick stylolitic intervals within the reservoir zones. Between Reservoir A and Reservoir B there is a dense limestone interval (30-50 ft), referred as dense layer in the Figure 1 well sections.
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