Variable Stimulated Reservoir Volume (SRV) Simulation: Eagle Ford Shale Case Study

Abstract

Production data analysis and reservoir simulation of the Eagle Ford shale are very challenging due to the complex characteristics of the reservoir and the fluids. Eagle Ford reservoir complexity is expressed in the enormous vertical and horizontal petro-physical heterogeneity, stress-sensitive permeability, and existence of multi-scale natural fracture and fault systems. This complexity makes the prediction of the geometry and conductivity of the hydraulic fracture resulting from the stimulation process rather challenging. On the other hand, reservoir fluid complexity is demonstrated in multi-phase flow, liquid loading in the wellbore, condensate banking, etc. Based on this complexity, 3D reservoir modeling and numerical simulation have the relative advantage of addressing irregular fracture geometry, variable SRV, and multi-phase flow aspects. The South Texas Asset Team at Pioneer Natural Resources is establishing a workflow for dynamic reservoir modeling that can integrate all reservoir/wellbore parameters (formation evaluation, drilling, completion, stimulation, pre-/post-fracture surveillance, and well performance data) in order to address key questions relating to field development; such as depletion efficiency, drainage area, wells interference, and condensate banking effects. In this paper, a case study is presented to demonstrate the integration of various measurements and surveillance data to build a variable SRV reservoir model. The variable SRV model described here has the following building blocks: 1) Formation evaluation: included all the reservoir characterization data derived from logs and 3D seismic inversions and structural attributes. 2) Surveillance data integration: microseismic data (backbone for this work) are integrated with chemical and radioactive tracer logs. 3) Well performance data integration: Production data is used to determine different flow regimes during the well history and to set bounds for stimulation parameters, such as fracture half-length and permeability ( √ ). 4) Numerical simulation: Micro-seismic attributes (density and magnitude) are converted to a permeability model after being calibrated with tracer logs and production flow regime parameters ( √ ). PVT data is matched against an Equation of State (EOS) and input into the model. Production data history matching, sensitivity and forecasting indicate the following: a) The SRV created by fracture stimulation has permeability fading away from the wellbore; b) Fracture geometry is variable and results in an irregular drainage area along the lateral; C) Onset of condensate banking near wellbore and along the fracture(s) can occur within the first year of production if draw down is not managed properly.

Seismic Inversion Based SRV and Reserves Estimation for Shale Plays

“…This observation differs from the general assumption thatfracture permeability diminishesaway from the wellbore. Our results show that during production the drainage volumes may encompass regions of high permeability instead of progressively moving towards lower permeability regions and thisshould be reflected in the flowing material balance plots suggested by Suliman et al (2013) and Samandarli et al (2011).…”

“…The advantage of our approach is that the SRV can be predicted with a high degree of confidence prior to fracturing using 3D seismic data. Although the SRV can be inferred solely from microseismic data as shown by Sinha (2017) and Suliman et al (2013), our approach allows us to predict the anticipated SRV prior to drilling or fracturing thereby providing better controls on the EUR.…”

A Validation Assessment of Microseismic Monitoring

“…A similar evaluation in horizontal wells is much more difficult because of the complexities of the completion and stimulation. Numerous studies of hydraulic fractures in horizontal wells have resulted in very complex distribution of microseismicity in shale reservoirs (e.g., Maxwell et al 2002;Daniels et al 2007;Waters et al 2009a;Ciezobka and Salehi 2013;Suliman et al 2013;Zeynal et al 2014;Virues et al 2015, etc. ), but none of these had ancillary data that actually validated the generation of a complex network.…”

Coupled Geomechanics and Fluid Flow Model for Production Optimization in Naturally Fractured Shale Reservoirs

“…The SRV permeability was used to represent the enhanced conductivity volume as a result of the secondary complex fractures activated during the stimulation treatment. It is assumed that the individual fractures link up to form a single connected fracture network with an effective permeability greater than the background matrix (Suliman et al, 2013;Rubin, 2010;Moncada et al, 2013). is evaluated after five years of production; the specific parameters used to calculate the NPV are discussed further in the next chapter.…”