For oil or gas fields with stratified reservoir layers, detailed productioncontribution for individual layer is always desired.Unfortunately, insome particular cases, production wells are completed following commingledscheme. This is worsened further if only very few production tests arerun for the field.This is the case for the Central Sumatera field withits 95 commingled production wells, among which only a few had undergoneproduction tests and none of them have ever undergone productionlogging.Problems rise when the occassion came in which detailedproduction contribution from individual reservoir layer is required for thefield's reservoir simulation modeling and productionevaluation/prediction. This paper presents an approach to solve the problem.The approach isbasically based on the application of soft computing (Fuzzy Logic) toinvestigate pattern of relationships between production contribution of layersin commingle wells and rock petrophysical data as well as other relevantgeological/engineering data.For the purpose, thirteen wells (key wells)that have production tests are assigned, among which three wells are assignedfor checking the validity of the recognised pattern.Using the validatedmost valid pattern, individual layer's production allocation for other wellsare determined with well-log analysis data as the major input. Result estimates for the candidate wells are better compared to resultsproduced by the conventional method of productivity index (PI)analogy.The resulted variation in water cut and separate oil and watersplit factors appear to be more realistic from any point of view. Introduction In managing a commingle production well, knowledge over productioncontribution of individual sand layer is always desired.The commonpractice performed during drilling and production activities of a productionwell is through the use of well testing/production testing and/or productionlogging. From the test, fluid dynamic data such as total liquid rate, water cut, and gas cut of an individual layer are produced.However, costand time efficiency is always used as the reason for not conducting suchtests. Therefore, even though such tests are always regarded as theprimary source of proof, an alternative means that can be used to provideestimates is always desired. Ideas of establishing a method that can provide illustration over productioncontribution of all layer(s) always exist.Certainly, there are approachesto serve the purpose such as productivity index (PI)/transmissibility analogyand petrophysical approach through fractional flow measurement in corelaboratory. However, those approaches are often considered inadequate foraccommodating various factors that may influence production contribution of aproductive layer. To materialize the requirement stated above, an indirect approach in theform of pattern recognition/modeling was taken.This approach was taken inorder to model relations between various factors in wellbore and productioncontribution of reservoir layers without being trapped by the certaincomplexity that may occur in any mathematical expressions trying to explain therelationships.For the purpose, fuzzy logic (a form of artificialintelligence) has been used.The choice is actually based on its capacityto accommodate both numeric and non-numeric data, since it is considered thatsome non-numeric data such as lithology and pore system also have someinfluence on production contribution.

Stratified reservoirs may have different types of heterogeneity in terms of grain size distribution in vertical direction. Geological surveys (i.e. using well logs) have long recognized the existence of fining upward and coarsening upward formations. In this study, such formations refer to as systems with decreasing upward and increasing upward permeability trends, respectively. Many waterflood candidate reservoirs have been found to follow either classification. However, the awareness of including this distribution classification as one of the screening criteria prior to waterflooding has not been established in the oil industry. A simulation study using a number of conceptual stratified reservoir models has been conducted. The results show that grain size distribution classification should have significant impact on waterflood performance. Each of the two classifications yields different effects on vertical sweep efficiency resulting from different crossflow mechanisms, which consequently gives different waterflood performance. It has been found that the oil recovery from waterflooding a reservoir with coarsening upward formation will always be higher than that from waterflooding exactly the same reservoir but with the opposite classification (i.e. fining upward formation) even though the two reservoirs are of the same level of heterogeneity (i.e. similar values of coefficient of permeability variation). In this study, the degree of heterogeneity effects on recovery were investigated as well as the magnitude of vertical-to-horizontal permeability ratio effects. Also, permeability noise was addressed as the reservoir may contain contrast permeability streaks in between the adjacent layers. A correlation has been derived based on the simulation results and has been proven to be able to predict simulation results with relatively good accuracy. Validations performed by comparison to actual production from a simple injection scheme (one injector and one producer) in Bangko Field, Indonesia, indicate that oil production rates obtained from the correlation show good agreement with those of production history. Introduction Heterogeneity plays an important role in predicting waterflood performance of a stratified reservoir. Heterogeneity may take place in both horizontal and vertical directions. In this study, we will consider the problem of vertical heterogeneity. One aspect of vertical heterogeneity is permeability variation. This situation may result from various geologic processes that took place during the sedimentation of the reservoir.1 The sedimentation of a certain reservoir rock may vary and take place at different geological time from that of another leading to a somehow unique classification of grain size distribution for the corresponding reservoir. This is the reason why a certain formation may exhibit a fining upward or coarsening upward grain size distribution classification. This phenomenon may be observed from the gamma ray log results of the reservoir, which is usually reported as the stratigraphic property of the reservoir. A fining upward grain size distribution indicates that the grain size of the rock becomes finer in an upward sequence, which consequently results in a decreasing upward trend of the permeability values. A coarsening upward grain size distribution, on the other hand, indicates a situation in which the grain size of the rock becomes coarser along the upward direction thus, an increasing upward permeability trend is observed. This will lead the formation to becoming a uniquely stratified reservoir. The effects of such types of heterogeneity on waterflood performance in reservoirs are investigated in the present study as well as the effect of the level of permeability variation. The effect of varied magnitude of crossflow is also studied by varying the value of vertical-to-horizontal permeability ratio (kv/kh). A simulation approach is applied in this study by using a numerical three-dimensional streamline simulator.

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Summary We developed a reservoir model for potential infill locations in this mature oil field. Prior to a 3D seismic survey expected recovery was over 60% and the field had a limited amount of reserves based on the old 2D seismic lines. The new seismic study showed the crest of the structure was missed during the initial field development. This study was commenced to delineate new well locations and determine if one or more horizontal wells were feasible. This study consisted of revising the petrophysics data from 11 wells, building an earth model and making a simple simulation model. The field had wells drilled over a fifteen year period from 1972 to 1987. This paper outlines the efforts to obtain a usable log suite including permeability, Swi and Sorw. Information from nearby fields and the initial field simulation using average properties were included in this analysis. This information was included into the earth model and subsequently the simulation model. Introduction When minimal data (wireline, core) are available in a given field, general concepts and approaches may be used to derive petrophysical answers that fit the overall model for the region in given stratigraphic layers. These answers (porosity, perm, irreducible water saturation and residual oil saturation) may be modified in the earth model building process to address shortfalls in preliminary simulation runs. This "looping" between the petrophysicist, the earth model builders and the simulation engineer provides a framework and process to achieve a coherent model result. In this case permeability and SCAL data are missing or exist in insignificant quantity, answers may be derived using available data and subsequently verified and supported with data from offset fields. Zones that are known to be at irreducible water saturation from supporting data (well tests, obvious sharp oil-water contact from resistivity and porosity) can be used to estimate irreducible water from Simandoux1 then use these answers in an empirically derived correlation between irreducible water, porosity and permeability, i.e. Coates2. This result may be verified with core data from offset fields. One caveat is the assumption that all rock types are represented within zones at irreducible water saturation. As with many oil fields in Sumatra, there exists a definitive relationship between increasing clay content and decreasing permeability. Zones at residual oil saturation below a moved oil-water contact seen in late-drilled wells in the field were used to estimate residual oil. These values are roughly verified by the water saturation verification via derived permeability. Missing wireline data may be synthesized from existing data. For example a rough relationship can often be found between bulk density and normalized gamma ray. It is very important to normalize the statistics of the gamma ray in a given interval that exists in the entire field for consistency. Pseudo curves can be used to insert reasonable values in intervals of poor data quality or if necessary used as a substitute curve. Background Menggala South field is located in central Sumatra (see Fig. 1). The structural closure is relatively small; only eleven wells had been drilled at the time of this study. Cumulative production was 53.4 MMBO as of September 2001 and original oil in place is estimated to be 94.8 MMBO. New 3-D seismic indicated a possible up dip location for a new well. Consideration was given to a horizontal well. To evaluate the horizontal well case, an earth model was created and simulation runs utilized to evaluate the potential of a horizontal well to reduce the possibility of water coning. A model was quickly built and simulations run, modifications made to input data and additional runs completed before making a recommendation.