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618 primary wellcontrol issue is temperature. If the pressure in the well is reduced suddenly and very high temperatures are present, the water in the hole will boil, accelerating the fluid above it upward. The saturation pressure, along with significant water hammer, can be seen at the wellhead. Thus, the most common method for controlling pressure in geothermal wells is by cooling through circulation. The need for extra casing strings in oil wells, as depth and the risk of overpressure increases, may cause the crossover between JAS oil and gas well average costs and predicted geothermal well costs seen in Figure 6.1 at 6,000 m. Because no known geothermal wells have been drilled to this depth, a cost comparison of actual wells cannot be made. 6.4 Predicting Geothermal Well Costs with the Wellcost Lite Model Chapter 6 Drilling Technology and Costs The completed wellcost data (JAS) show that an exponential fit adequately describes completed oil and gas well costs as a function of depth over the intervals considered using only two parameters. The correlation in Figure 6.1 provides a good basis for estimating drilling costs, based on the depth of a completed well alone. However, as the scatter in the ultradeep wellcost data shows, there are many factors affecting well costs that must be taken into consideration to accurately estimate the cost of a particular well. The correlation shown in Figure 6.1 has been validated using all available EGS drilling cost data and, as such, serves as a starting point or base case for our economic analysis. Once more specific design details about a well are known, a more accurate estimate can be made. In any case, sensitivity analyses were used to explore the effect of variations in drilling costs from this base case on the levelized cost of energy (see Section 9.10.5). 6.4.1 History of the Wellcost Lite model There is insufficient detailed cost history of geothermal well drilling to develop a statistically based cost estimate for predicting well costs where parametric variations are needed. Without enough statistical information, it is very difficult to account for changes in the production interval bit diameter and the diameter, weight, and grade of the tubulars used in the well, as well as the depths in a given geological setting. Although the correlation from the JAS data and drilling cost index discussed above allow one to make a general estimate of drilling costs based on depth, they do not explain what drives drilling costs or allows one to make an accurate estimate of drilling costs once more information about a drilling site is known. To do this, a detailed model of drilling costs is necessary. Such a model, called the Wellcost Lite model, was developed by B. J. Livesay and coworkers (Mansure et al., 2005) to estimate well costs based on a wide array of factors. This model was used to determine the most important driving factors behind drilling costs for geothermal wells. The development of a wellcost prediction model began at Sandia in 1979 with the first wellcost analysis being done by hand. This resulted in the CarsonLivesayLinn SAND 812202 report (Carson, 1983). The eight generic wells examined in the model represented geothermal areas of interest at the time. The handcalculated models were used to determine well costs for the eight geothermal drilling areas. This effort developed an early objective look at the major cost categories of well construction. The initial effort was followed by a series of efforts in support of DOE wellcost analysis and costof power supply curves. About 1990, a computerbased program known as IMGEO (Petty, Entingh, and Livesay, 1988; Entingh and McLarty, 1991), which contained a wellcost predictive model, wasPDF Image | Drilling Technology and Costs
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