Analysis Of South Pars Gas Field's Well Test Data

Well testing is widely used in oil/gas field exploration and exploitation both in home and abroad,and it has become an important means in reservoir recognition and evaluation,production performance monitoring and completion efficiency assessment.Well testing is an imperative work in scientifically exploiting oil and gas field.Well testing data is a necessary,which must be available in oil and gas field evaluating,establishing and adjusting development program.Presently,there is a discrepancy existing in internal well testing area,and that is a plenty of new theories can't transform into new technologies,which are badly needed in field or can't offer the service for field.Considering this problem,it is necessary to improve the traditional explanation model,establish the theoretical model which is really needed,more close to practical gas reservoir and more suitable for the application.of gas reservoir in order to serve for production.In this article,based on the theory of fluid mechanics in porous medium,we founded the bottom hole pressure dynamic response mathematic modal in homogeneous reservoir and double porosity reservoir.By using Green's function,and applying the techniques of laplace transformation,we obtained the analysis solution in laplace domain.By using Visual Basic program we obtained the academic type curve.And we analyzed the characters of fluid flow through porous medium and its influencing factor of those type wells.Based on the theory part,we explained the specific gas wells.By using new methods,re-evaluate a part of gas wells' former well test data,comparing with former well test result,determining feasibility,re-understanding gas reservoir,instructing the exploitation of gas field.By doing research of this article,we have established a more practical theoretical model which makes the well testing explanation more close to the practical conditions,resolved the problem that the theoretical model doesn't match the real conditions and improved the well testing data's rationality and reliability.