PREDICTION OF WELL BEHAVIOR USING WELL DELIVERABILITY ANALYSIS - , DEWI ASMOROWATI ([Yogyakarta] : Universitas Gadjah Mada, 2013)
Geothermal energy comes from the heat in the earth. The word geothermal comes from the Greek words âgeoâ, meaning earth, and âthermeâ, meaning heat. People around the world use geothermal energy to produce electricity, to heat homes and buildings, and to provide hot water for a variety of uses (The NEED Project, www.need.org). In operating geothermal field, the thing to note is the sustainable production as well as problems that affect it. The problem that often occurs in the geothermal field is scale, corrosion and mechanical problems. Scale and corrosion problems are closely related to the charactheristic of the production fluid and the pressure drop caused by the production process. Pressure drop in the reservoir can be seen from the decreasing of wellhead pressure, but to look at the cause of production rate changing is not easy because we do not know what is actually happening in the reservoir to the surface. For that we need a study to predict the problems that occur around the bottom hole to the surface, so that preventive measures can be taken later or proper treatment. One technique that can be used to predict the problems that occur around the bottom hole to the surface is using the well deliverability analysis method. With this analysis we can predict the well behavior and problems that occur around the bottom hole to the surface. Analysis is done by data and curve interpretation of deliverability curve based on Grant experiment. Purposes of this study are analizing qualitatively and quantitatively the deliverability curve to predict the problem that occurs in a well, so it can give some recommendation to minimize or prevent the problem. This study takes data from two Geothermal Fields, X and Y Geothermal Field. Data from X Geothermal field contains 3 wells, they are well A, well B, and well C and from Y Geothermal field contains 4 wells they are well D, well E, well F and well G. The calculating data of well A and B are obtained by inputting some well and reservoir permeability and pressure drop between reservoir and feedzone. The calculating data of well D, E, F and G are obtained from WellSim simulator. The input data in WellSim are tracer flow test (TFT) data, well geometry, fluid type, maximum and minimum flow and reservoir pressure. The calculating is done to get deliverability curve and then the interpretation of the graph based on Grant experiment. From the analysis, well A, D and F show production decline. The deliverability curve of well A shows the production decline for about 60 â 85%, it is caused by moving of the flashing point, it shows from the increasing of dryness value from 7-14% in 1997 become 13-15% in 2010. The deliverability curve of well D shows the production decline for about 50%, it is caused by declining of reservoir pressure that can be seen from the decreasing of feed zone pressure in 1997 is 27 bar and in 2010 is 20 bar. The deliverability curve of well F shows the production decline for about 29%, it is caused by moving of the flashing point. Scale problem can be reduced by acidizing or by closing the well for a few moments and then opened again by reducing the size of the choke diameter orifices by using a smaller plate. Closing the well for a while also can increase the reservoir pressure and temperature, so it also can help the well to control the reservoir pressure. In addition, the reservoir pressure also can be controlled by good planning of water injection. Maintaining or controlling the reservoir pressure also can prevent the moving of the flashing point.