An expiditive tool to identify reservoir fluids using Downhole Fluid Analyzer data | |
| Author | Juthamas Wongprachanukool |
| Call Number | AIT Thesis no.GE-12-11 |
| Subject(s) | Downhole--Fluid Analyzer Reservoirs--Thailand, Gulf of |
| Note | A thesis submitted in partial fulfillment of the requirements for thedegree of Master of Engineering inGeotechnical and Earth Resources Engineeringwith Area of Specialization in Geosystem Exploration and Petroleum Geoengineering, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. GE-12-11 |
| Abstract | During the past ten years, more and more oil and gas fields have been discovered in the Gulf of Thailand. These reservoirs need to be evaluated properly due to fluid complexity to make a costeffective and efficient. There are many academic published papers that show successful application of Downhole Fluid Analyzer (DFA), whichprovides with real-time measurement of reservoir fluid properties such as composition of five component groups, gas/oil ratio (GOR) and live fluid density with accuracy and reliability.The purpose of this work is to find a way to identifyreservoir fluid from reliable Downhole fluid analyzer data in real time monitoring using anexpiditive and simpletool with Excel spreadsheet. The advantages of using Excel spreadsheet is that the real time monitors can identify the reservoir fluid at once easier and the job planners can organize and control their jobs more effectively.The basic inputs from DFA measurements are weight percentages of CO2, C1, C2, C3-5and C6+, GOR and density. This study used decomposition method proposed byJulian et al.(2008) to split the composition in weight percentage as group into individual component. Molecular weight calculation and unit conversion are also contained in thespreadsheet for converting the individual components. The reservoir fluids were identified into five types, namely black oil, volatile oil, retrograde gas, wet gas and dry gas, based ona combination of four criteria, whichare McCain’s Generalizations, Gradient Ranges, Fluid Classification and Rough Guideline.The results are compared with the laboratory measurement and showeda good accord. Furthermore, an artificial neuron network (ANN) is established that can be applied to predict the missing reservoir fluid composition, (which is N2in weight percentage in this study) based onthe DFA measurements of fluid compositions data as input. The ANN model is validated and compared with the PVT laboratory tests. The predictionsare in good agreement with the laboratory testresults. This ANN model was also ableto predict N2for other wellswith agood agreement beingreached. |
| Year | 2013 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. GE-12-11 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Civil and Infrastucture Engineering (DCIE) |
| Academic Program/FoS | Geotechnical Engineering (GE) |
| Chairperson(s) | Pham Huy Giao; |
| Examination Committee(s) | Noppadol Phien-wej;Saifon Sirimongkolkitti; |
| Scholarship Donor(s) | Royal Thai Goverment Fellowship;Asian Institute of Technology Fellowship; |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2013 |