Recent success of commercial shale gas developments in a number of basins throughout North America can be attributed to the application of advanced technologies used to drill horizontal wellbores, stimulate the shale reservoir, and optimize productivity of shale-bearing formations. Many of the drilling and completion techniques learned from the thousands of wells drilled and stimulated in more mature shale basins, such as the Barnett, have been applied to newer shale discoveries such as the Marcellus and Haynesville in the United States, and both the Montney and the remarkable Horn River Basin in Canada. The unique properties of each shale, however, preclude a "cookie-cutter" development approach from being applied. Each play must be optimized as a unique reservoir.In order to utilize numerical simulation as a tool in optimizing well design, one needs to develop a model that appropriately represents the complex process of gas flow from the native reservoir to the hydraulic fractures and subsequently to the wellbore. This is challenging due to poor understanding of variables such as pressure dependent permeability variation, fluid cleanup, relative permeability effects, non-Darcy flow, methane desorption in a nano-Darcy shale matrix, and fracture conductivity variations from the dominant hydraulic fractures to the secondary induced and natural fractures. Another challenge is accurate representation of the hydraulic fracture. Is the fracture planar or complex? What is the fracture geometry? What is the fracture intensity within the stimulated volume? What is effectively propped? What is the proppant distribution within the fracture system? How does this tie to effective conductivity and does it vary with distance from the wellbore (three dimensionally)?Finding a unique match to historical production is very challenging. Shale gas operators collect a large amount of data including cores and logs (specialized for nano-Darcy rock), micro seismic, diagnostic fracture injection testing (DFIT), fluid and proppant tracers and more. This data is used to better characterize the reservoir and the natural and hydraulic fractures and can help to constrain model inputs.This paper discusses a workflow used in developing a numerical shale gas model for Nexen's Horn River shale gas reservoir. Presented is a practical and systematic approach to using surveillance data; specifically microseismic data in construction of the stimulated reservoir volume (SRV) and the network of hydraulic fractures in the model. Discussions will also focus on accurately modeling complexities such as non-Darcy flow in the hydraulic fractures, pressure-permeability dependencies, variations in hydraulic fracture conductivity and fluid cleanup. The objective is to gain understanding and insight into the uncertainties that have the greatest impact on well performance.

We present a systematic approach to integrate geoscience and dynamic reservoir modeling for two multiwell pads in the Horn River basin, Canada. The Horn River shale-gas play is a worldclass unconventional gas resource and is being exploited by use of multistage hydraulic fracturing along horizontal wells. The two well pads, Pad-1 and Pad-2, selected for this study comprise eight and seven horizontal wells, respectively, with 1 to 7 years of production history. Numerical modelling of shale reservoirs has historically been a problematic low-confidence exercise because of the difficulties associated with inadequate characterization of the geologic framework of shale plays; the problems of estimating the properties of induced-fracture networks; and the complexities of capturing multiphase flow in fracture networks and wellbores during production, especially in the face of offset-well activity. This paper provides insights into these issues.The geoscience modelling activity begins with integrating information from cores, well logs, petrophysical analyses, and seismic data into a 3D geocellular model. At first, the model is built upon a simple lithostratigraphic concept, which is the basis of the numerical-flow-modelling exercise of Pad-1. The 3D geocellular model is thereafter thoroughly reworked to incorporate a sequence-stratigraphic perspective to the Horn River shale. This reworked geocellular model has a profound impact on the dynamic modelling of Pad-2. Also, hydraulic conductivity of induced and natural fractures is measured on Horn River core plugs at reservoir conditions to constrain conductivity values assigned to primary, secondary, and tertiary flow paths into dynamic reservoir modelling.As a result of the integrated work flow, we have achieved a history match allowing us to further understand the hydraulic-fracturing behaviour and its impact on producing shale reservoirs of the Horn River formation. On the basis of the findings, we recommend targeting the Evie and Otter Park shale reservoirs for landing horizontal wells and multistage fracturing when the Carbonate Fan is thin; this approach can produce all three compartmentalized shale reservoirs of the Horn River formation. Ultimately, the objective of any reservoir modelling project is to provide a range of reliable forecast of future performance that is grounded in representative geoscience interpretations and that takes operational constraints into account. The technical learnings described in this work will be helpful to further understand hydraulic-fracturing behaviour and its impact on producing shale reservoirs.

This paper presents a systematic approach to integrate geoscience and dynamic reservoir modeling of two multi-well pads in the Horn River Basin, Canada. The Horn River shale gas play is a world-class unconventional gas resource and is being exploited using multi-stage fracturing along horizontal wells. The two well pads, Pad-1 and Pad-2, selected for this study are comprised of eight and seven wells respectively with 1 to 7 years of production history. Numerical modeling of shale reservoirs has historically been a problematic low-confidence exercise, because of the difficulties associated with inadequate characterization of the geologic framework of shale plays; the problems of estimating the properties of fracture networks; and the complexities of capturing multi-phase flow in fracture networks and wellbores during production, especially in the face of offset wells activity. The work presented in this paper provides useful insights into these issues.The geoscience modeling activity begins with integrating information from cores, well logs, petrophysical analyses and seismic data into a 3D geocellular model. At first, this model was based upon a simple lithostratigraphic concept and this was the basis of the numerical flow modeling exercise of Pad-1. The 3D geocellular model was thereafter thoroughly reworked to incorporate a sequence stratigraphic perspective of the Horn River shale and to include geomechanical considerations based upon the stratigraphic positioning and landing depths of the subject horizontal wells. This reworked geocellular model had a profound impact on the dynamic modeling of the Pad-2. Also hydraulic conductivity of induced and natural fractures was measured on core plugs at reservoir conditions to assign conductivity values to primary, secondary and tertiary flow paths into dynamic reservoir modeling.As a result of the integrated workflow, we have achieved a history match allowing us to further understand the hydraulic fracture behaviour and its impact on producing shale reservoirs within the Horn River Formation. Based on the findings we recommend completion strategy that can produce more than one compartmentalized shale reservoir to optimize production. Ultimately, the objective of any reservoir modelling project is to provide a range of reliable forecast of future performance that is grounded in representative geoscience interpretations and that takes operational constraints into account. The technical learnings described in this work will be helpful to further understand the hydraulic fracturing behaviour and their impact on producing the Horn River shale reservoirs.

Heavy oil reservoirs often require thermal enhanced oil recovery (EOR) processes to improve the mobility of the highly viscous oil. When working with steam flooding operations, finding the optimal steam injection rates is very important given the high cost of steam generation and the current low oil price environment. Steam injection and allocation then becomes an exercise of optimizing cost, improving productivity and net present value (NPV). As the field matures, producers are faced with declining oil rates and increasing steam oil ratios (SOR). Operators must work to reduce injection rates on declining groups of wells to maintain a low SOR and free up capacity for newer, more productive groups of wells. Operators also need a strong surveillance program to monitor field operational parameters like SOR, remaining Oil-in-Place (OIP) distribution in the reservoir, steam breakthrough in the producers, temperature surveys in observation wells etc. Using the surveillance data in conjunction with reservoir simulation, operators must determine a go-forward operating strategy for the steam injection process. The proposed steam flood optimization workflow incorporates field surveillance data and numerical simulation, driven by machine learning and AI enabled Algorithms, to predict future steam flood reservoir performance and maximize NPV for the reservoir. The process intelligently determines an optimal current field level and well level injection rates, how long to inject at that rate, how fast to reduce rates on mature wells so that it can be reallocated to newly developed regions of the field. A case study has been performed on a subsection of a Middle Eastern reservoir containing eight vertical injectors and four sets of horizontal producers with laterals landed in multiple reservoir zones. Following just the steam reallocation optimization process, NPV for the section improved by 42.4% with corresponding decrease in cumulative SOR by 24%. However, if workover and alternate wellbore design is considered in the optimization process, the NPV for the section has the potential to be improved by 94.7% with a corresponding decrease in cumulative SOR by 32%. This workflow can be extended and applied to a full field steam injection project.