Dynamic Behavior of Discrete Fracture Network (DFN) Models

Abstract

This work shows that discrete fracture network modeling is very desirable for the characterization of naturally fractured reservoirs but it is only a highly subjective starting point. Thus, calibration against short and long term pressure transient tests is most crucial. This paper shows how the dynamic behavior of a discrete fracture network model of Margarita gas field compared against pressure transient measurements in a sidetrack delineation-well. The performance comparison of a very fine-grid reservoir model, which included the discrete fracture network information, versus a much coarser upscaled grid model is also documented. Introduction Geological reservoir characterization is the most crucial first step in construction of a credible reservoir flow model for naturally fractured reservoirs. The most notable approaches for constructing a geological model of a reservoir include classical deterministic methods, where geologists make the best interpretation from existing data and build a model of the reservoir. In the last several years, however, deterministic approaches have been complemented by quantitative geostatistical approaches such as multipoint statistics (MPS) and discrete fracture network (DFN) modeling.1,2,3 The MPS generates a depositional and lithofacies model of a reservoir by incorporating geological architecture and properties of the rock fabric and is touted as a very promising stepping stone in construction of viable numerical reservoir flow models.1 However, for reservoir modeling purposes, the flow units and fracture flow paths must be included in the depositional model.4 The DFN modeling is often used to accomplish the latter. Both the deterministic and geostatistical techniques, however, are highly subjective and, while such fundamental geological modeling techniques are often the necessary starting points, any revisions toward the construction of the ultimate reservoir flow model would depend on calibration against reservoir performance as well as carefully designed flow tests.5 In fact, an ultimate reliability standard for viability of any reservoir characterization model is calibration against dynamic data from several wells. This implies that reservoir characterization life cycle is an iterative process as new static and dynamic data become available. Experience in naturally fractured reservoirs (NFR) has indicated that low permeability formations often produce fluids through open fractures, which intersect the wellbores. This paper illustrates fluid flow through individual fractures intercepted by a vertical well, initially estimated using a parallel plate method, finding that contribution of each fracture need to be calibrated to a production test. The composite summation of individual fractures along the wellbore allowed the construction of a synthetic PLT profile. A horizontal sidetrack from the vertical well was planned to improve the well's productivity. A discrete fracture network (DFN) model, generated around the area of influence of the well, was used to identify the best direction for the sidetrack interval (perpendicular to the most open main fracture planes) and to predict the type of the fractures to be intercepted. After the sidetrack was drilled, fracture types (hierarchy by aperture) were identified and classified using an ultra sonic borehole image tool. The DFN fracture model was calibrated against a pressure buildup test conducted in the sidetrack well, using dual-porosity numerical simulation over a very fine grid model. The calibration studies showed that the aperture-based effective transmissivity was significantly greater than the actual well test transmissivity while the estimated fracture storativity was significantly lower than the actual well test value. Dynamic upscaling of the fine-grid model was performed to evaluate whether the upscaled models would preserve the well/reservoir behavior of the fine-grid models. Margarita Field General Characteristics The Margarita structure is located in the southern Bolivian Subandean, on the structural trend of the Suaruro Range, 35 km to the west of Villamontes town.6–8 The Margarita Field lies in the Caipipendi Block (Fig. 1) operated by Repsol-YPF with 37.5% equity. Partners are BG International (37.5%) and Pan American BP (25%). The field is located in the northern part of an elongated anticline oriented NNE-SSW, and is 30 km long and 9 km wide. The closure of the field consists of several compartments separated by reverse faults.

Evaluating Factors Controlling Damage and Productivity in Tight Gas Reservoirs

“…The simulation results shown in Figure 18 indicate a significant reduction in cumulative gas production in the case that the natural fractures are damaged. The examples have shown very large skin factors in wells where natural fractures are damaged (Araujo et al, 2005), confirming the reliability of the simulation results.…”

“…The solids may penetrate only a short distance into the rock matrix and cause only a shallow mechanically damaged zone. However the damage to natural fractures and open permeable conduits can be severe, as drilling fluids invasion mostly occurs through the natural fractures (Sharif, 2007;Araujo et al, 2005).…”

“…Natural fractures in tight formations are very sensitive to solid invasion damage as the natural fractures generally have small aperture. In sandstone reservoirs, since acidising is normally impractical, the damage may not be removed (Araujo et al, 2005).…”

Multilevel Fracture Network Modeling of Naturally Fractured Reservoirs

“…4 displays a DFN representation of multilevel fracture sets in a quartzite reservoir. 8 These examples demonstrate the complexity of fracture morphology in real reservoirs and hence highlight the difficulty of constructing viable numerical models for naturally fractured reservoirs. …”

“…9 The flow models constructed around the DFN reservoir characterization are natural extensions of the classical dual-porosity approach, but the fracture characterization demand for such models is extensive and requires calibration against flow measurements in the field. 8,10,11 Finally, there is a strong interest in an appropriate level of upscaling reservoir data from the fine scale to a computationally accurate coarse scale. This can be accomplished effectively with the use of dual-mesh and streamline-based reservoir models.…”