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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.

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One of the most significant elements in the successful development of an unconventional multi-layered horizontal shale play, such as the Permian Basin Wolfcamp and Spraberry shales, is the continuous refinement of completion design programs in order to account for the specific geological characteristics of the target formation and spatial considerations. This paper examines a number of well completions, a large percentage of which are multi-stacked, over a four-year period in the Permian Basin. In this paper, we describe the ways in which a completion design program has been constantly refined in the development of Wolfcamp and Spraberry formations over certain areas of Midland Basin. Key performance drivers, such as regional pore pressure, rock properties and stresses, well spacing, etc. along with multidisciplinary data sets such as microseismic, tracers, bottom hole reservoir pressure, and production history are incorporated into modeling the completion designs. Details of the workflow, analysis of results and implementation of the Permian multi-stack design optimization plan are presented. The paper includes lessons learned from early vintages to modern completions, parent/child mitigation strategies, and future ideas to further optimize the operations. Information presented in this paper will help multi-disciplinary asset teams, consisting of engineers, geologists, analysts and managers; design a workflow for selecting appropriate design solutions for their individual shale assets.

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Determination of proper well spacing is a key factor in successful resource play development. The objective is to optimize the spacing between wells that effectively stimulates the reservoir rock volume, increases overall recoveries and minimizes interference between wells. Recent work by Pioneer Natural Resources, Inc. (PXD) to optimize well spacing along the Eagle Ford Shale trend has resulted in reduced lateral spacing as well as vertical staggering of wells within the reservoir interval. Numerous tools are used to aid the determination of well spacing across the play, and it is critical to integrate data from all the tools available when establishing a spacing recommendation. The workflow presented is used to establish spacing recommendations through integration and analysis of multi-source data collected during the lifecycle of an unconventional resource play.The well spacing toolkit includes data collected from multiple disciplines in the asset. Microseismic data is integrated with 3D seismic data and fracture models to derive stimulated rock volumes. RA tracer and chemical fluid tracer data collected during well stimulation provide information on propped fracture length and height and inter-well communication, respectively. Pressure data recorded during well stimulation and interference tests performed after stimulation are used to help understand changes in well communication over time. Oil and gas geochemistry data from wells drilled in multiple stratigraphic intervals are used to provide insight on fracture height growth.The ability to integrate and analyze multi-source data is essential when establishing a recommendation on a complex problem such as proper well spacing; the workflow presented allows for the manipulation and visualization of these data in 4D that can be interpreted and edited by multiple users.