CHAPTER III. METHODOLOGY III.1. REASONING METHODOLOGY Analytical reasoning method which used in this study are: Deductive accumulative method: Reservoir connectivity can be evaluated from geological, geophysical data, fluid analysis data, reservoir properties data and production data as applied on other reservoir in different field (Cosentino (2001), Smalley (1996), etc), and that method should be applicable in studying reservoir connectivity in G057B and G058B zones reservoir of Nilam Field, Kutei Basin in East Kalimantan. Statistic method: Data plotted in graphical curve for trend analysis and interpretation. III.2. BACKGROUND THEORY OF RESERVOIR HETEROGENEITY (CONNECTIVITY) Snedden et al. (2007), mentioned that surveying the general terrain of reservoir connectivity reveals significant differences among companies and academics in how it is defined, measured, modeled, and acted upon. However, almost all agree that connectivity is a function of the field structural framework, reservoir stratigraphy, and fluid characteristics. Further on, Snedden et al. (2007) suggested that approach to reservoir connectivity is to define two entities: static and dynamic connectivity. Static connectivity describes the native state of a field, prior to production start-up. Evaluation of static connectivity is the basis for proper assessment of original hydrocarbons in place and prediction of fluid contacts in un-penetrated compartments. Dynamic connectivity describes movement of fluids once production has begun. Initiation of production actually perturbs the original fluid distributions as pressure and saturation changes proceed in a non-systematic fashion across field compartments. Barriers and baffles play a varying role over the field life, in some cases breaking down over time, suggesting that these are not true compartment boundaries. Analysis of dynamic connectivity is essential to estimating ultimate recovery from a field. A compartment is precisely defined as a trap which has no internal boundaries (e.g., faults, channel margins) which would allow fluids to reach equilibrium at more than one elevation (over geologic time scales). Connections between compartments include fault juxtaposition windows and erosional scours between channels. Snedden et al. (2007) approach also already mentioned before by Cosentino (2001), reservoir heterogeneities are small to large geological features, that may or may not be significant from a strictly static reservoir characterization point of view, but do have a significant impact on fluid flow. Therefore, reservoir heterogeneity is not, or at least only, a truly static issue. 6
A reservoir is intrinsically heterogeneous. Difference in lithology, texture and sorting as well as the presence of fractures, faults, baffles and diagenetic effects of different nature are the principal factors responsible for what we call, with general term, reservoir heterogeneity. The existence of these features affects the fluid flow at different scales, from the micro to the megascale. Small-scale heterogeneities can virtually always be recognizable on the available core material. At the scale of pore (micro scale), heterogeneities are basically related to the occurrence of a mixture of pore types. This is clearly observed in carbonate system, where different types of primary and secondary porosity are often associated. At the scale of cores (macro scale), heterogeneities are often related to lamination and crossbedding. In fact, from a sedimentary point of view, the only depositional unit that can be considered intrinsically homogeneous is the lamina. Being the product of a single, geologically instantaneous depositional event, the lamina is internally free of significant heterogeneities. Figure 6. Classification of reservoir heterogeneity (Cosentino, 2001) Ideally, small-scale heterogeneities should be explicitly taken into account, and proper up scaling procedures should be applied to preserve at higher scale the impact of such heterogeneities on fluid flow. This phase, however, can be very time consuming, since it requires the use of numerical modeling to correctly describe the process and derive adequate pseudo functions. 7
In, practice, it is very seldom performed and the facies are characterized at the macro scale with average petrophysical values that are computed without much concern about small scale heterogeneities. The implicit assumption is therefore that the rock can be considered homogeneous at smaller scale. While this assumption is in most cases the only practical approach, the presence and the impact of small scale heterogeneities should not be neglected a priori. In particular, parameters like permeability anisotropy (Kh, Kv) and residual oil saturation (Sor) should be investigated, since they may prove to be relevant in the global dynamic behavior of the reservoir. Large-scale (mega scale) heterogeneities are the most important types of internal reservoir discontinuity. The can represent barriers to fluid flow and be responsible for what we can refer to as the compartmentalization of the reservoir. Alternatively, they may represent preferential flow paths with respect to a homogenous, lower permeability background rock. In either case, the impact in the reservoir dynamics may be so strong to dominate field performance; therefore their assessment is a mandatory task in all reservoir studies. The main types of large scale heterogeneities are faults, either sealing or not, boundaries of genetic units, high or low permeability streaks and shale baffles. Fractures, either open or sealed, represent another important type of reservoir heterogeneity. Smalley and England (1994), a reservoir may be compartmentalized laterally by sealing faults or lateral variations in reservoir quality. Vertical compartmentalization occurs where reservoir zones are separated lateral extensive zones of flow permeability rocks, such as shale, carbonate cemented zones or tar mats. Compartmentalization is part of the natural anatomy of a reservoir that controls the spatial distribution of reserves and the optimal way to produce them. In term of identifying the presence of reservoir heterogeneity or reservoir connectivity, both static and dynamic data are involved. Cosentino (2001) further on grouped those static and dynamic data into following groups: 1. Geophysics. 2D and 3D seismic surveys are the most important source of information as far as internal reservoir description concerned. They are more relevant early in the field life, when information coming from other disciplines is scarce. Other and more sophisticated geophysical techniques, like Vertical Seismic Profiles and cross-well seismic can be collected later in the field life, with the specific objective of clarifying particular areas of the reservoir. Seismic data can be used to identify and locate structural and stratigraphycal features which could generate internal compartmentalization, even though they do not provide information about sealing capability of such heterogeneities. 2. Fluid data. Differences in fluid contact depths and spatial variations in oil and formation water compositions can normally be detected early in the field life, through the analysis 8
of the collected samples. These differences may be the expression of a reservoir compartmentalization. 3. Well testing. Traditionally, wells were tested to determined produced fluids, borehole damage, deliverability and some basic reservoir parameters like pressures and permeability. Pressure transient test are performed throughout the field life, with the objective of assessing well deliverability and damage. When good quality data are available, this test can also provide useful information about the internal reservoir geometry. In some cases, unconventional tests like extended well testing are performed early in the field life, with the specific objective of evaluating reservoir continuity and reducing the risk related to unfavorable reservoir connectivity. 4. Production data. Well production performance is often the ultimate and most reliable source of information about reservoir compartmentalization. Unfortunately, these data become available when the development phase is in its final stage or has already been finalized. For the reason, they are useful especially in later reservoir studies, e.g. when the implementation of a secondary project is under consideration. Other important information for reservoir connectivity study is the understanding of geology and stratigraphy of particular study area. Also need to consider is the well history information such as spud date, completion date, production date etc. Following is general workflow of the study to understand the reservoir connectivity. (Figure 7) Geology N Geophysics Well History Fluid Data Res. Properties Reservoir Connectivity Map Matching Geological Concept & Res. Pressure trend Production data Y Reservoir Characterization Figure 7. General workflow of the study Reservoir Model 9
III. 3. DATA COLLECTION All data used in the study was acquired and gathered from Nilam Field and its belong to VICO Indonesia. In brief detail; log and ELAN volume data from 250 wells, 275 km2 processed seismic 3D data, one core description data, three gas crhromatography results, and MDT/RFT pressure and production results. III.4. DATA PROCESSING Data seismic 3D load into Seiswork software and prepared for seismic section and seismic attribut interpretation. Wireline log data (density, neutron, GR, etc) loaded into Stratwork software and stratigraphically correlated. Permeability data retrieve from ELAN Volume result then load and plotted in a permeability map. Gas chromatography analysis data loaded and plotted in starplot chart. Pressure taken from pressure measurement during production life, loaded and plotted in particular chart. Gas rate taken from production data then load and plotted with pressure data. III.5. DATA ANALYSIS Seismic analysis done with interpretion of seismic data in cross section and attribute maps. (Figure 8) Gas chromatography data plotted in starplot and interpreted. (Figure 9) Stratigraphy and sedimentary environment(s) interpretation from correllation of wireline log. (Figure 10) Pressure data (RFT/MDT and BHP ) from development and production plotted and later analyze to see the pressure decline trend. (Figure 11) Reservoir connectivity map plotted and edited by using Zmap software from Landmark. (Figure 12) Source: VICO Internal report (Butterworth, 2005) Figure 8. Example of seismic attribute analysis (amplitude attribute analysis) used for reservoir connectivity study. Red line is interpreted border of fluvial channel system represent a reservoir. 10
Hydrogen Sulfide Heptanes 1000 Plus 100 Hexanes 10 n-pentane iso-pentane RESERVOIR A Hydrocarbon Analysis n-butane 1 iso-butane Carbon Dioxide Nitrogen Propane Methane Ethane Hydrogen Sulfide Heptanes 1000 Plus 100 Hexanes 10 n-pentane iso-pentane n-butane RESERVOIR B Hydrocarbon Analysis 1 iso-butane Carbon Dioxide Nitrogen Propane Methane Ethane Figure 9. Example of star plot of gas chromatography data analysis from reservoir A and reservoir B, showing different gas chromatography results as those two reservoirs are separated. Tank X Tank Y Source: VICO Internal report (Butterworth, 2005) Figure 10. Example of correlation analysis for reservoir connectivity study 11
Pressure (psi) Reservoir Connectivity 2009 Figure 11. Example of pressure decline trend analysis of two reservoirs in presence of two decline trends (Cosentino, 2001) Source: VICO Internal report (Butterworth, 2005) Figure 12. Example of reservoir connectivity map resulted from geological correlation. 12