(607e) Recent Trends of Streamline Simulation Technology Applications in Oil Industry

Streamline and streamtube methods have been used in fluid flow computations for many years. Early applications for hydrocarbon reservoir simulation were first reported by Fay and Pratts in the 1950s. Streamline-based flow simulation has made significant advances in the last 15 years. Today’s simulators are fully three-dimensional and fully compressible and they account for gravity as well as complex well controls. Most recent advances also allow for compositional and thermal displacements.

In this paper, we present a comprehensive review of the diverse applications of streamline simulation technology in petroleum industry. This paper offers a comprehensive review of the major development in this area. This work involved the review of more than 200 technical papers and gives a chronological advancement of streamline simulation technology from 1996 till now. In view of the fact that this state of-the-art technology has been employed for a wide range of applications, we have defined three major application areas that symbolize the relevance and validity of streamline simulation in addressing petroleum engineering issues. These are history matching, reservoir management and upscaling, ranking and characterization of fine-grid geological models.

We have reviewed many of the various applications of streamline simulation to history matching, ranging from the simple use of streamline-delineated drainage zones in traditional history matching practices, to sophisticated use of streamline information for data integration. One major benefit of streamline simulation is that due to its efficiency, many more flow simulations can be evaluated in a given time compared to standard finite difference simulators. As a consequence, many authors have employed streamline simulation in history matching algorithms which require hundreds or thousands of flow simulations. Another significant benefit of streamline-based history matching is instantaneous identification of flow directions and drainage regions of the producing wells. Thus, when a producing well does not match its historical data at a particular time, the streamlines can associate the mismatch with the petrophysical properties in a particular drainage zone.

Streamline-based history matching techniques can go beyond the ability to delineate drainage zones and by employing direct methods. Direct methods were developed first by Wang and Kovscek (2000), and employ the fractional flow curves at the producing wells and relate them to changes in effective permeability (and/or porosity) values for history matching.  Another history matching technique that we discuss in our review is the gradient-based optimization methods which rely on the calculation of sensitivity coefficients. By drawing a correspondence between streamline modeling and ray tracing in seismology, Vasco et al. (1998) developed a method for analytically calculating the sensitivity coefficients of the time-of-flight with respect to the grid-block petrophysical properties. The time-of-flight can then be related to breakthrough of injected fluids or tracer. The fact that sensitivity coefficients are calculated analytically greatly enhances the efficiency of the optimization procedure. Because the sensitivity calculations involve evaluation of 1D integrals along streamlines, it scales very well with respect to model size (Qassab et al. 2003).  This breakthrough development has led to a significant body of research and applications on history matching. A separate approach which is distinctive from the optimization methods described above fall under the category of data integration methods. These methods use the same streamline-specific information which is use for the direct methods, but instead of applying the history matching modifications directly to the grid block properties, the information is incorporated into the geomodeling process. As such, these methods are termed “geologically-consistent”.

We have also reviewed many of the different applications in which streamlines have been exploited to optimize flood management. In our review paper, we have identified four areas under this application area namely sweep efficiency, rate optimization, well placement and enhanced oil recovery. The review period has observed intense efforts by researchers especially during the last three years to fully and widely implement streamlines technology to address reservoir management issues.

A key element to successful flood is good sweep efficiency, which can be significantly impacted by reservoir heterogeneity. Traditionally; most flood management has been restricted to static allocations or sensitivity studies centered on finite difference simulation (Thiele and Batycky 2003, Izgec et al. 2010). Static allocations have been used by engineers to quantify injector to producer relationship. However, such approach did not permit the evaluation of changes in injection profiles, water influx and cross flow over time (Grinestaff 1999). Likewise, detailed finite difference simulation can provide some answers related to injectors/producers connectivity but computational times and data extraction makes well level decisions on a daily basis too expensive (Grinestaff 1999).

As an alternative, streamline simulation is an ideal tool to help optimize large floods because it provides efficient and fast means to capture detailed fluid movements resulting from conformance control (vertical and/or areal by mechanical means), infill drilling, well conversion and pattern re-alignment (Grinestaff 1999, Thiele and Batycky 2003, Izgec et al. 2010). Thus, total flow rates and phase rates between well pairs can be calculated. This important information help identify areas of extreme cycling, patterns with poor sweep, or local voidage imbalances (Thiele and Batycky 2003).

Additionally, we reviewed the literature to identify the various applications in which streamlines has been employed for bridging the gap between fine scale detailed geological models and flow simulation models through providing a judicious upscaling technique and appropriate ranking criteria to select a subset of equi-probable reservoir realizations.

Recent enhancements in software, hardware, integration expertise, data collection and interpretation have improved the industry's ability to build detailed 3D large multi-million cell geologic models of the subsurface (Idrobo et al. 2000). Due to their computational time and costs, upscaling is needed to efficiently and reasonably translate detailed grids to coarser grids without compromising the anticipated reservoir performance (Chawathé and Taggart 2001). Equally, there exists a need to rank a large number of generated reservoir model realizations to quantify the potential sources of errors or uncertainties. For both tasks, streamlines method is especially appealing. The streamline time-of-flight provides a powerful means of visualization. By comparing the time-of-flight from the fine scale detailed geologic model with that from an upscaled model, the validity of upscaling techniques are examined in addition to level of upscaling (Datta-Gupta 2007). Additionally, a unique feature of streamlines method is its ability to efficiently compute the sensitivity of the production data to reservoir parameters such as porosity and permeability providing a great potential for considering a large number of plausible reservoir descriptions (Efendiev et al. 2008).

Streamline simulation has undergone several phases within its short stretch in the petroleum industry. Initially, the main focus was on the speed advantage and less on fluid flow physics. Next, the focus was shifted to extend its applicability to more complex issues such as compositional and thermal simulations, which require the inclusion of more physics, and potentially reducing the advantage of computational time. Recently, the focus has shifted towards the application of streamline technologies to areas where it can complement finite difference simulation such as revealing important information about drainage areas, flood optimization and improvement of sweep efficiency, quantifying uncertainties, etc. 

References

Chawathe, A. and Taggart, I. 2001. Insights into Upscaling Using 3D Streamlines. Paper SPE 66379 presented at the SPE Reservoir Simulation Symposium, Houston, Texas, 11-14 February. http://dx.doi.org/10.2118/66379-MS.

Datta-Gupta, A. and King, M.K. 2007. Streamline simulation: theory and practice. Textbook Series, SPE, Richardson, Texas.

Datta-Gupta A. 2000. Streamline simulation: a technology update. J. Pet Tech 52 (12): 68-73, 84. SPE 65604. http://dx.doi.org/10.2118/65604-MS.

Efendiev, Y., Datta-Gupta, A., Ma, X. and Mallick, B. 2008. Modified Markov Chain Monte Carlo Method for Dynamic Data Integration Using Streamline Approach. Math Geoscience 40: 213-232.

Grinestaff, G.H. 1999. Waterflood Pattern Allocations: Quantifying the Injector to Producer Relationship with Streamline Simulation. Paper SPE 54616 presented at the SPE Western Regional Meeting, Anchorage, Alaska, 26-27 May. http://dx.doi.org/10.2118/54616-MS.

Izgec, O., Sayarpour, M. and Shook, G.M. 2010. Optimizing Volumetric Sweep Efficiency in Waterfloods by Integrating Streamlines, Design of Experiments, and Hydrocarbon F-{capital Phi} Curves. Paper SPE 132609 presented at the SPE Western Regional Meeting, Anaheim, California, USA, 27-29 May. http://dx.doi.org/10.2118/132609-MS.

Idrobo, E.A., Choudhary, M.K. and Datta-Gupta, A. 2000. Swept Volume Calculations and Ranking of Geostatistical Reservoir Models Using Streamline Simulation. Paper SPE 62557 presented at the SPE/AAPG Western Regional Meeting, Long Beach, California, 19-22 June. http://dx.doi.org/10.2118/62557-MS.

Qassab, H., Khalifa, M., Pavlas, R., Afaleg, N., Ali, H., Kharghoria, A., He, Z., Lee, S.H., Datta-Gupta, A. 2003. Streamline-based Production Data Integration Under Realistic Field Conditions: Experience in a Giant Middle-Eastern Reservoir. Paper SPE 84079 presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, 5-8 October. http://dx.doi.org/10.2118/84079-MS.

Thiele, M.R. and Batycky, R.P. 2003. Water Injection Optimization Using a Streamline-Based Workflow. Paper SPE 84080 presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, 5-8 October. http://dx.doi.org/10.2118/84080-PA.

Vasco, D.W., Yoon, S. and Datta-Gupta, A. 1998. Integrating Dynamic Data into High-Resolution Reservoir Models Using Streamline-Based Analytic Sensitivity Coefficients. Paper SPE 49002 presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, 27-30 September. http://dx.doi.org/10.2118/49002-MS.

Wang, Y. and Kovscek, A.R. 2000. A Streamline Approach for History-Matching Production Data. Paper SPE 59370 presented at the SPE/DOE Improved Oil Recovery Symposium, Tulsa, Oklahoma, 3-5 April. http://dx.doi.org/10.2118/59370-MS.