In this study, the non-intrusive embedded discrete fracture model (EDFM) in combination with the Oda method are employed to characterize natural fracture networks fast and accurately, by identifying the dominant water flow paths through spatial connectivity analysis. The purpose of this study is to present a successful field case application in which a novel workflow integrates field data, discrete fracture network (DFN), and production analysis with spatial fracture connectivity analysis to characterize dominant flow paths for water intrusion in a field-scale numerical simulation. Initially, the water intrusion of single-well sector models was history matched. Then, resulting parameters of the single-well models were incorporated into the full field model, and the pressure and water breakthrough of all the producing wells were matched. Finally, forecast results were evaluated. Consequently, one of the findings is that wellbore connectivity to the fracture network has a considerable effect on characterizing the water intrusion in fractured gas reservoirs. Additionally, dominant water flow paths within the fracture network, easily modeled by EDFM as effective fracture zones, aid in understanding and predicting the water intrusion phenomena. Therefore, fracture clustering as shortest paths from the water contacts to the wellbore endorses the results of the numerical simulation. Finally, matching the breakthrough time depends on merging responses from multiple dominant water flow paths within the distributions of the fracture network. The conclusions of this investigation are crucial to field modeling and the decision-making process of well operation by anticipating water intrusion behavior through probable flow paths within the fracture networks.
Multi-stage hydraulic fracturing has recently gained strong interest in unconventional plays in the Middle East due to high natural gas production potential. However, prevalent characteristics of the area, including high-pressure / high-temperature (HPHT) conditions and presence of complex natural fracture networks, pose significant challenges to reservoir characterization. These challenges have motivated the development of an integrated workflow using microseismic data for the characterization of reservoir properties resulting from the interaction between natural and hydraulic fractures. This study proposes a reliable method for modeling hydraulic fractures from scarce microseismic data. Initially, a microseismic model—based on field records of microseismic data and natural fracture spatial characterization—was developed. Issues related to limited microseismic data availability were tackled through combination of a probabilistic algorithm, Gaussian Mixture Model, and a DFN model. Then, the resulting synthetic microseismic events enabled the generation of a hydraulic fracture model using the embedded discrete fracture model (EDFM) and an in-house microseismic spatial density algorithm that captured major hydraulic fracture growth tendencies. Next, the created hydraulic fracture geometries were validated against a physics-based hydraulic fracture propagation model. Lastly, a single-well sector model—based on a corner point grid that honored the original 3D discrete fracture network (DFN)—was history matched, confirming the successful application of the proposed methodology.
Reliable estimates of hydraulic fracture geometry help reduce the uncertainty associated with estimated ultimate recovery (EUR) forecasts and optimize field developing planning in unconventional reservoirs. For these reasons, operators gather information from different sources with the objective to calibrate their hydraulic fracture models. Microseismic data is commonly acquired by operators to estimate hydraulic fracture geometry and to optimize well completion designs. However, relying solely on estimates derived from microseismic information may lead to inaccurate estimates of hydraulic fracture geometry. The objective of this study is to efficiently calibrate hydraulic fracture geometry by using microseismic data, physics-based fracture propagation models, and the embedded discrete fracture model (EDFM). We first obtain preliminary estimates of fracture geometry based on microseismic events’ spatial location and density with respect to the perforation cluster location. We then tune key completion parameters using an in-house fracture propagation model to provide hydraulic fracture geometries that are constrained by the microseismic cloud. In the history matching process, we included the effect of natural fractures, using the microseismic events location as natural fracture initiation points. Finally, we used cutoff coefficients to further reduce hydraulic fracture geometries to match production data. The results of this work showed a fast and flexible method to estimate fracture half-lengths and fracture heights, resulting in a direct indicator of the completion design. Additionally, hydraulic-natural fracture interactions were assessed. We concluded that the inclusion of cutoff coefficients as history matching parameters allows to derive realistic hydraulic and natural fracture models calibrated with microseismic and production data in unconventional reservoirs.
Hydraulic fracture models are useful mechanisms to understand reservoir properties and performance in unconventional reservoirs. The inclusion of field measurements can further refine hydraulic fracture models by providing key information to optimize field development planning. Microseismic data remains as one of the most accessible types of field data, which can help understanding reservoir extension. Additionally, microseismic data is useful to assess well interference and extracting average hydraulic fracture geometry. However, an efficient method to extract cluster-based hydraulic fracture models from microseismic data is absent due to loss of spatial accuracy of current techniques when used in reservoir simulations. Moreover, accessibility to hydraulic fracture propagation simulators may add up to the challenge of producing reliable and fast estimates of fracture geometry. In this paper, our main objective is to efficiently reconstruct a hydraulic fracture model using microseismic data and combine it with a history matching workflow in order to optimize field development planning in a shale gas well. We created a fast and efficient cluster-based hydraulic fracture reconstruction tool, which uses microseismic events’ spatial distribution to create hydraulic fracture models represented by the embedded discrete fracture model (EDFM). Our workflow can include different sets of discrete fracture network (DFN) models, in order to assess their interactions with hydraulic fractures. We used two different DFN models: original DFN model and DFN represented by the activation of natural fractures during well stimulation. Finally, we calibrated our hydraulic fracture and DFN models to production data. We efficiently modeled the effective hydraulic fracture geometry that contributes to production by including spatial cut-off coefficients, which reduce fracture geometry in the history matching process. The results of this novel workflow produce not only efficiently calibrated hydraulic fracture models, but provides valuable insights regarding hydraulic fracture geometry and their interaction with natural fractures. Additionally, the inclusion of cut-off coefficients allows to model the effective contribution of hydraulic fractures after calibrating fracture geometry with production data.
During the unconventional reservoir development, a proper modelling of the underground fracture networks and their effects on production is crucial for reservoir development potential and realistic economic analysis. Conventionally, the complex fracture system formed by hydraulic and natural fractures is extremely difficult to capture, let alone to numerically simulate it. Most importantly, the current best solution can only rely on the knowledge of the natural fractures from the geology and geophysics team and hydraulic fractures from the engineering team. Nevertheless, this solution fails to realize the dynamic stress regime variations when fracturing jobs are done within the horizontal wellbore. In this study, a variety of data source and modelling tools is harnessed to delineate a more realistic and representative discrete fracture network (DFN). The first step is to obtain the original natural fractures already depicted from geological and geophysical information and the statistical information regarding the spatial configurations of this DFN. Next, a new set of natural fractures is generated by an in-house natural fracture generator while preserving the spatial characteristics of the original natural fractures at the same time. Then, a combined DFN of the original natural fracture and newly generated natural fractures is accomplished. This combined DFN is then intensity-calibrated by the given microseismic cloud events, especially focusing on the near-wellbore region. Then, a displacement discontinuity method- (DDM-) based in-house hydraulic fracture propagation model is used to generate hydraulic fractures with complex boundaries, honoring the fracturing job logistics from the engineering team. After this step, an ultimate and highly representative DFN can be achieved. By applying this very novel workflow, DFN characterizations of both a single-well scenario and well-pad (3 wells) scenarios have been highly successful. Statistics such as the cluster-wise hydraulic fracture half-length, height, aperture, and numbers of activated/nonactivated natural fractures can be easily presented. Through the powerful numerical method called the embedded discrete fracture model (EDFM), production simulation and stimulated reservoir volume evaluation can be seamlessly studied. Extents of 3D drainage volumes can also be plotted with ease. Overall, a holistic picture regarding the unconventional reservoir’s underground DFN can be reliably depicted, using the proposed workflow.
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