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Wang, Chun-Chin (Department of Safety, Health and Environmental Engineering, Hungkuang University) | Wen, Chih-Chung (Department of Safety, Health and Environmental Engineering, Hungkuang University) | Jhang, Shu-Huei (Department of Safety, Health and Environmental Engineering, Hungkuang University) | Yang, Cheng Hung (Department of Safety, Health and Environmental Engineering, Hungkuang University)
This paper presents an example of how expert systems can be developed and used for planning structural piece-part production. First, expert systems are briefly and generically described. Then the production processes within a shipyard-like structural piece-part production facility are defined within an expert system “shell”; that is, the “objects,” “attributes,” and “rules” describing the production process are established and explained. Then various structural piece-parts are described to the system and the system identifies the required production processes for each described part. The inference process underlying the identification of these processes is described for each of these parts. Finally, potential applications of expert systems to other areas of shipbuilding operations are discussed.
The joint surface roughness is one of the important parameters which influences the mechanical and hydro-mechanical behavior of rock joints. In most cases, the joint roughness coefficient (JRC) was used to quantitatively express the roughness degree. In this study, we applied the mean Z2 values of all profiles on the joint surface, rather than picking out one or several typical profiles, as estimated parameter to predict the JRC values. The calculated Z2 values and JRC values diminish with the increase of sampling intervals. Through analyzing the measured JRC values under three sampling intervals, the 1.0 mm interval is pointed out the most appropriate one to evaluate the joint roughness.
In addition, we evaluated and compared the JRC values of joints within granite specimens that have different mechanical properties and weathering state by two different methods (Z2 method and backward analytical method). From obtained results, The Z2 method could relatively accurately predict the JRC values of unweathered material at 1.0 sampling interval, while overestimate the JRC values in weathering state. It may be attributed to that the weather process may weaken the mechanical properties of JCS values and basic friction angle which are the controlling mechanical factors of definitional JRC values. Moreover, the Z2 roughness metric does not take into consideration the mechanical proprieties and topography characteristics of weathering rock mass. In total, Z2 method should be improved by considering additional parameters related to mechanical properties of rock joint (i.e. distribution of contact area within joint surface).
It has long been recognized that the rock joint is one of the crucial components of a rock mass. The rock joint surface plays a significant role in controlling the mechanical and hydro-mechanical behavior. In previous study, a parameter of joint roughness coefficient (JRC) is proposed to quantitatively express the roughness degree (Barton and Choubey, 1973). Nevertheless, measuring definitional JRC values still needs a tilt test or direct shear test to make a reliable estimation. To overcome this limitation of experiments, ten typical profiling lines are defined and visual comparisons are made to estimate roughness degree (Barton, 1977). However, this method is strongly dependent on the subjectivity and experience level of the researcher. To remove this bias, the root mean square first derivative values (Z2), a statistical numerical parameter, is revealed, which is also easily to be determined.
Summary Gases such as carbon dioxide, nitrogen, and methane that can be present in a steam-assisted gravity-drainage (SAGD) steam chamber (but do not condense into the liquid phase to any large degree at reservoir conditions) are referred to as noncondensable gases (NCGs). The coinjection of NCGs with steam during SAGD results in changes in production rate, total oil production, and the amount of steam required to mobilize the bitumen in place. To investigate the impact of NCGs on SAGD performance by means of numerical simulation, it is important to model gas solubility in both bitumen and water accurately. Also, the dependence of relative permeability on temperature needs to be accounted for to achieve reliable results. This study presents a systematic approach to predict the K-values for the gas/bitumen- and gas/water-phase equilibria over a wide range of pressures and temperatures.
Defining petrophysical and mechanical properties of target and barrier zones are key components of the hydraulic fracture modeling process; subsequently, the selection of the detail necessary to accurately model fracture/reservoir performance is challenging. This work investigates whether using detailed petrophysical and mechanical properties provides fracture design parameters that better represent actual fracture behavior and subsequent well performance than a single-layered model.
The approach was to model an existing hydraulic fracture treatment and well performance from a well located in the northern Delaware Basin producing from the lower Brushy Canyon Formation. Models varied from a single layer model with simple-averaged, petrophysical properties to a fine resolution 1-ft model with detailed petrophysical values. Detailed core descriptions were constructed to appropriately represent the thin-bedded and micro-laminated sandstones and siltstones.
In addition, point load tests measured values of fracture toughness for specific lithofacies from 600 to 1100 psi-in½. In comparison, the default value for a sandstone system is 1000 psi-in½. Other mechanical properties, e.g., Poisson’s ratio and Young’s modulus were derived from well logs, and were within typical values.
For the fracture modeling phase, the actual treatment volumes, rates and pressures were inputted into the model along with the measured petrophysical and mechanical properties. Model net pressure was matched with the actual values to verify the output. The dimensionless fracture conductivity (FCD) from the various models ranged from 4.8 to 13.6. The range depends on the variation of lithofacies included in the fine resolution models and their associated mechanical/petrophysical properties. Adding micro-laminated and bioturbated siltstones at the expense of clean sandstone in the finer resolution models resulted in higher permeability, fracture toughness and lower stress gradient.
For the production history matching phase, simulation pressures were significantly overestimated compared to actual measured bottomhole pressures for all single layer models regardless if actual or default mechanical properties were used. The overestimation reflects a threefold increase in pore volume due to the single layer values. For the finer resolution 1-ft model, the simulation pressure was significantly below measured pressure values using default mechanical properties. However, using actual mechanical properties in the 1-ft resolution model resulted in an increase in the FCD due to the decrease in fracture toughness and stress gradient input values. As a result, a very good match was obtained between simulation and actual pressures; indicating the 1-ft model with the measured mechanical properties is a good representation of the actual reservoir system.