Viscous and heavy oil reservoir is a challenge in oil production due to unfavorable mobility ratio. Thermal injection is a method to increase well productivity and oil recovery which is usually performs in shallow reservoir. One type of thermal injection is cyclic steam stimulation. Cyclic steam stimulation was conducted in two wells of Melibur field as a pilot project. Since it has 8–12 cP viscosity, 22–26° API oil gravity and relatively shallow reservoir depth, Melibur has an appropriate character to perform steam injection. The project started with well selection with the main consideration is amount of remaining oil in reservoir. It is also considering completion diagram and operational aspect for each well. The injection process was performed in 10–12 days with certain injection parameter to meet the heat requirement for reservoir and follow with 5 days soaking. This paper focuses on the result and effect of cyclic steam stimulation to well and offset wells production rate and fluid properties. Many experiences acquired from the project of cyclic steam stimulation perform in Sihapas formation, one of them is the effect to offset well that indicates there is a connection and high heat conductivity between wells. Incremental of initial production rate about 40% occurred in first well. In second well, this operation gives an effect to offset well with the incremental of production rate reach 100% in nearest well. Oil properties changes with different in viscosity, oil gravity and pour point value after cyclic steam stimulation.

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Permeability estimation for un-cored wells is a classic issue. A simple model that widely used is using core porosity-core permeability cross plot to determine the linier regression. Then we estimate permeability in un-cored well after making adjustment for porosity log to porosity core. The difficulties using that method is most of cross plot did not show clear relationship (scatter data) due to effect of rock heterogeneity. Therefore another effort is needed by separate it based on rock type to get better relationship. Permeability itself is not depend only on porosity but also other properties like clay content, grain size, tortuosity and etc. Part of this phenomenon had been modeled by Carmen-Kozeny which illustrates strong dependency of permeability on average grain size, tortuosity and flow zone index. The conventional way to reduce data scatter is by using additional correlation parameters. Commonly shale content (Vsh) and reservoir facies are used to give reliable transform or regression analysis for estimating the permeability. Start with this concept; we try to simplify the correlation by modified the Carmen Kozeny eq. (used flow zone index term) using clay content as another parameter that influences permeability value. Because we assume that porosity and clay content are the most important properties that have significant effect on permeability. In this paper, we will describe permeability estimation for un-cored well as function of porosity and clay content using modified flow zone index-permeability cross plot. This cross plot has been test in three clastic reservoirs in Lower Sihapas formation either for consolidated or unconsolidated sandstone. The result shows this cross plot give better relationship compare to conventional cross plot and more simple transform to estimate permeability in un-cored well for input to geologic and reservoir simulation models. Introduction EMP Malacca Strait has develop reservoir integrated study which involves data review, G & G remodeling, reservoir characterization, reservoir modeling and prediction to identify the reliable hydrocarbon potential and developing a reliable reservoir model for choosing the optimum development plan for Lower Sihapas formation in three clastic reservoir1,2. One of the main subjects during the integrated study workflow is the reservoir rock characterization and core data analysis which includes the permeability transform to develop correlation between core data and log data. Permeability is one of the fundamental rock properties which represent the quality of a reservoir. The appropriate permeability value in each well is needed in order to represent the permeability distribution in a reservoir model. Usually the data that we have from cored well is very limited. Beside that, in order to get the data from each well we need to perform coring process that quite time consuming and require expensive laboratory measurements. For that reason, in term of practical use, we need a mathematical function that can represent the permeability value from each key well (cored well) by using the existing data (log data).

A 1000 bopd was achieved from successful integrated study of Kurau Field in 2007 year. All best practices of the study describe in this paper. The objective of this study is to build a complete reservoir model from an integrated study involving all aspects of engineering & G&G, such as data review, G&G modeling, reservoir characterization, reservoir modeling and prediction, in order to optimize the development of Kurau Field. The milestone starts from interpreting the 3D seismic data, structural or zonation model, petrophysics analysis, reservoir characterization, static modeling, dynamic modeling, and proceeds until it reaches the development and performance prediction of Kurau Field. More than just a simple model, Kurau reservoir model resulted from all data and results from G&G such as interpretation of 3D seismic, G&G modeling (static model), environmental correction for well logs from Petrophysical Software analysis together with reservoir engineering works such as rock and fluid properties analysis, rock characterization and also production engineering aspects. Geomodeling software was used to build 3D seismic to reservoir modeling, with additional Simulation Software to complete the simulation. The history matching analysis shows a satisfactory level of production profile comparison results of model performances with actual production data. Several infill wells and even EOR potential can be identified from this reservoir simulation model. Using an integrated study workflow from G&G, Petrophysic and Reservoir Engineering work had greatly improved the reliability of this reservoir model. Recently updated, those infill wells proved able to yield an additional 1000 bopd to Kurau field, as previously predicted. An integrated study from Engineering & G&G Department was required when we attempted to build a reservoir model. Such a model can be used to determine infill well potential, work over result estimation and also the EOR project. Achieving an increment of the oil recovery factor is the most beneficial result from this reservoir simulation. Introduction Nowadays, reservoir simulations play an important role in the petroleum industry. Geological modeling, reservoir characterization, well performance analysis, infill well proposal and even EOR methods simulation can be applied. While it would seem to be more complicated than conventional techniques, reservoir simulation can on the other hand achieve or at least a yield preliminary pictures of the entire reservoir. With so many software releases available at present, the user just picks whichever is most suitable, depending on needs, compatibility with currently used software and also, of course, on the budget. The important rule to be borne in mind is that "garbage in, garbage out". Kurau Field in the EMP-Malacca Strait has been generated into a reservoir simulation. Within a 9 months' integrated study, a team can achieve a reliable reservoir simulation. Beginning with a 3D seismic interpretation and then combining this with a geological interpretation and reservoir characterization, the team can also generate a reasonable history matching. Geomodeling Software and Simulation Software were exploited to generate and run the model. Kurau Field itself has never been modeled before. The structure was quite complicated. Even it was only from the Lower Sihapas Formation, reservoir characterization plays an important role when a model is built. The new geophysics interpretation to define faults was carried out. Further, sand correlation was involved, along with reinterpretation due to new data from previous infill wells.

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