Abstract This paper presents the development of a static model for a naturally-fractured High-Pressure-High-Temperature (HPHT) gas sand reservoir located in the Tarim basin, Western China. The study is part of a well placement optimization study. It is motivated by the big challenges of drilling a well at depths ranging from 6800m-8000m[AG1] in a HPHT environment. A detailed fine-scale model is required as input for the dynamic model. The static model is developed through an integration [AG2]process. It consists of both matrix and fractures. The matrix modeling started by integrating 3D seismic and log data to build the structural model. A new rock type scheme was developed by reconciling log and core data, including capillary pressures. Additionally, permeabilities are estimated at each uncored location using a two-step approach, namely trend estimation by regression analysis and variability simulation by 1D Gaussian simulation. The 3D modeling was executed in the order of least dependent to most dependent variable (i.e., from facies, to rock type, then followed by porosity, permeability and saturation respectively). From the geology, the sand bodies were interpreted to be continuous throughout the field. Discontinuous mudstone layers are sandwiched in-between the sand bodies. This information, together with outcrop data, is used to guide the spatial relationships in the model. Facies, rock type, porosity and permeability are simulated using geostatistical procedures. Meanwhile, saturation is generated based on the Leverett J-Function. To quantify the uncertainty in the various data, especially in the capillary pressure data, the porosity-permeability relationship, the gas-water contact and the surface tension of the gas-water system, a probabilistic model of the Gas Initially in Place (GIP) is created through uncertainty and sensitivity analysis. The origin of the fracture system was analyzed by developing a prototype of a conceptual model. The understanding from the prototype model is coupled with the 3D seismic, outcrops, drilling information, rock mechanics, image log, core, and dynamic data, to develop fracture characteristics and correlations. The discrete fracture system is modelled using a stochastic simulation approach, constraining it to the seismically-inverted fracture density map for each zone through well-seismic correlations and a nonlinear inversion [AG3]to build the Discrete Fracture Network (DFN). Finally, the fracture model is integrated with the matrix model by upscaling the DFN model into the grid system. Following the creation of the static model, a dual porosity model was prepared for dynamic modeling by maintaining consistency between the fine scale and upscaled models throughout the upscaling process. The methodology described above has produced a detailed fine scale model that shows consistency between properties and geology. This is a direct consequence of the new rock type system and the order in which the simulation was conducted. The facies model shows the continuity of the sand bodies, and the discontinuity of the mudstone, as indicated by the geological interpretation. The 3D Poro-Perm relationship shows the variability which is a reflection of the variability of the core data. The probabilistic distribution of the GIP is in agreement with the results of conventional reservoir engineering analyses, namely Material Balance and Rate Transient Analysis. Furthermore, the fracture distribution confirms the information both at the wells, as well as in-between the wells as given by the seismic interpretation. This study demonstrates that a reliable fine scale model can be developed to match the available data and interpretation by properly preparing the pre-requisite inputs and following the order of dependency in the reservoir attributes.

Abstract This paper presents the implementation of a novel approach for efficient well placement design based on the dynamic characteristic of the reservoir without the need to run flow simulation. The approach uses State-of-the-Art Technology called the Fast Marching Method (FMM) coupled with Geometric Pressure Approximation to define a Dynamic Reservoir Quality Map. This map is also referred to as the Depletion Capacity (DC) Map. It provides the mapping of future potential locations to further deplete the reservoir. The DC Map is generated by calculating 4 important factors, namely Pore Volume, Mobility of the Hydrocarbon, Reservoir Energy, and the Undrained Volume of the reservoir. The major breakthrough in using this technology is the ability to estimate the pressure distribution (i.e., reservoir energy) and drainage volume (i.e., to estimate the undrained volume) efficiently. These are the 2 factors that are difficult to obtain without conducting traditional flow simulation. The two aforementioned factors were obtained by calculating the FMM diffusive time of flight which can be related to the pressure drop by the Geometric Pressure Approximation theory. Thus, in effect, this calculation represents a pseudo-simulation, which is orders of magnitude faster than conventional simulation. The technique is applicable for both fine scale and coarse scale models with large number of realizations representing geological uncertainties. This approach works well in capturing the primary depletion phenomenon. In this paper, we demonstrate the evaluation of existing well placement of an actual developed field, with 16 wells, located in Tarim Basin, West China, by comparing it to a new design. Additionally, the method is also used to propose future locations for infill drilling. The study is motivated by the big challenges faced when drilling a well at a depth between 6800 m–8000 m. Optimum well placement has the potential to drill optimum number of wells to produce the same reserves. The new design was created with a scenario where 5 exploration wells that been put into production for a couple of years to represent the early depleted condition. The results show the optimum design can be achieved with only 12 wells. A saving of 4 wells compared to the existing well pattern. This is a significant saving considering the drilling challenges. For the infill drilling, the study shows that for this reservoir, adding more wells may not be beneficial from the ultimate recovery point of view but production can be accelerated by drilling up to 2 more wells at the best potential locations as suggested by the DC Map. Best of all, the proposed method optimizes the locations very quickly.

This paper describes a fracture characterization and modeling project in China. The fracture characterization uses different sources and different scales of fracture-related data including outcrop, core, log, seismic, drilling, well test and production data for fracture recognition and analysis (Prioul et al (2009) and Hirata, (1989)). Subsequently, in the fracture modeling phase, a geostatistical method – the discrete modeling approach – is utilized to integrate the data from the various fracture characterization results. The fracture modeling uses the fracture characteristic parameters and their corresponding distribution under the guidance of the regional fracture development background to build a discrete fracture network (DFN) (Figure 1). Presentation Date: Thursday, September 28, 2017 Start Time: 9:45 AM Location: 330A Presentation Type: ORAL