Integrated fracture characterization of Asmari reservoir in Haftkel field

The Asmari reservoir in Haftkel field is one of the most prolific naturally fractured reservoirs in the Zagros folded zone in the southwest of Iran. The primary production was commenced in 1928 and continued until 1976 with a plateau rate of 200,000 bbl/day for several years. There was an initial gas cap on the oil column. Gas injection was commenced in June 1976 and so far, 28% of the initial oil in place have been recovered. As far as we concerned, fracture network is a key factor in sustaining oil production; therefore, it needs to be characterized and results be deployed in designing new wells to sustain future production. Multidisciplinary fracture evaluation from well to reservoir scale is a great privilege to improve model’s accuracy as well as enhancing reliability of future development plan in an efficient manner. Fracture identification and modeling usually establish at well scale and translate to reservoir using analytical or numerical algorithms with the limited tie-points between wells. Evaluating fracture network from production data can significantly improve conventional workflow where limited inter-well information is available. By incorporating those evidences, the fracture modeling workflow can be optimized further where lateral and vertical connectivity is a concern. This paper begins with the fracture characterization whereby all available data are evaluated to determine fracture patterns and extension of fracture network across the field. As results, a consistent correlation is obtained between the temperature gradient and productivity of wells, also convection phenomenon is confirmed. The findings of this section help us in better understanding fracture network, hydrodynamic communication and variation of temperature. Fracture modeling is the next step where characteristics of fractures are determined according to the structural geology and stress directions. Also, the fault’s related fractures and density of fractures are determined. Meanwhile, the results of data evaluation are deployed into the fracture model to control distribution and characteristics of fracture network, thereby a better representation is obtained that can be used for evaluating production data and optimizing development plan.

Ductile fracture modeling of metallic materials: a short review

Since the end of the last century a lot of research on ductile damaging and fracture process has been carried out. The interest and the attention on the topic are due to several aspects. The margin to reduce the costs of production or maintenance can be still improved by a better knowledge of the ductile failure, leading to the necessity to overcome traditional approaches. New materials or technologies introduced in the industrial market require new strategies and approaches to model the metal behavior. In particular, the increase of the computational power together with the use of finite elements (FE), extended finite elements (X-FE), discrete elements (DE) methods need the formulation of constitutive models capable of describing accurately the physical phenomenon of the damaging process. Therefore, the recent development of novel constitutive models and damage criteria. This work offers an overview on the current state of the art in non-linear deformation and damaging process reviewing the main constitutive models and their numerical applications.

Technology Focus: Hydraulic Fracturing Modeling (November 2021)

In the past decades, the success of unconventional hydrocarbon resource development can be attributed primarily to the improved understanding of fracture systems, including both hydraulically induced fractures and natural fracture networks. To tackle the fracture characterization problem, several recent papers have provided novel insights into fracture modeling technique. Because of the complex nature and heterogeneity of rock discontinuity, fabric, and texture, the fracture-modeling process typically suffers from limited data availability. Research shows that modeling results reached without interrogation of high-resolution petrophysical and geomechanical data can mislead because the fluid flow is actually controlled by fine-scale rock properties. A more-reliable fracture geometry can be obtained from an enhanced modeling process that preserves the signature from high-frequency data. Advanced techniques to model fracturing processes with proppant transportation and thermodynamics require even more-sophisticated simulation and computation power. When the subsurface is too puzzling to be described by a physical model and existing data, machine learning and artificial intelligence can be adapted as a practical alternative to interpret complex fracture systems. Taking a discrete fracture network (DFN) as an example, a data-driven approach has been introduced to learn from outcrop, borehole imaging, core computed tomography scan, and seismic data to recognize stratigraphic bedding, faults, subseismic fractures, and hydraulic fractures. Input data can be collected by hand, 3D stereophotogrammetry, or drone. When upscaling DFN into a coarse grid for reservoir simulation, deep-learning techniques such as convolutional neuron networks can be used to populate fracture properties into a dual-porosity/dual-permeability model approved to yield high accuracy compared with a fine-grid model. Furthermore, the new techniques greatly extend the application of fracture modeling in the arena of the energy transition, such as in geothermal optimization. Recommended additional reading at OnePetro: www.onepetro.org. SPE 203927 - Numerical Simulation of Proppant Transport in Hydraulically Fractured Reservoirs by Seyhan Emre Gorucu, Computer Modelling Group, et al. SPE 202679 - Deep-Learning Approach To Predict Rheological Behavior of Supercritical CO2 Foam Fracturing Fluid Under Different Operating Conditions by Shehzad Ahmed, Khalifa University of Science and Technology, et al. SPE 203983 - A 3D Coupled Thermal/Hydraulic/Mechanical Model Using EDFM and XFEM for Hydraulic-Fracture-Dominated Geothermal Reservoirs by Xiangyu Yu, Colorado School of Mines, et al.