Sedimentary environments characterization of Yu2 horizon through a modern complex of well logs in central regions of West Siberia basin

Karpov I.A.

Novosibirsk State University, Novosibirsk, Russia

[e-mail]

Yu₂ regional petroleumphereness horizon (upper Bathonian stage) is one of the object for resources increasing and oil production on the West Siberia central region with well developed infrastructure. This horizon has a difficult reservoir rocks distribution, which deals with big amount of sedimentation environments in it’s forming time [3].

Core sample material discrecity reduces information volume which important for detailed paleogeographic reconstructions. These reconstructions help us make a sand bodies with good reservoir properties prediction. However, wireline data availability and hole well information continuity allow geologists to use well log materials not only for facial map creation, and for construction sand reservoirs bodies prediction. Sedimentation environments characterization based on modern complex of well logs is research actuality for this investigation.

Research technique is based on detail sedimentological investigations of core material from Yu₂ horizon of 80 wells. From the results of textural and mineralogical investigations we know a sedimentary environments [1]. These environments should be characterized by statistical analysis of well log data from each well in Yu₂ horizon interval.

Almost all wells consist a spontaneous potential method (SP). SP is a standard method for West Siberia petroleumphereness basin. SP data were converted in aSP data by the next formula:

аSP=1-[(SP-SP_min)/(SP_max-SP_min)]

After that aSP data were analyzed and histograms were constructed by the next methodology:

1. Subprogram was written for making illustration of interpretation process in MS Excel 2007 software. This subprogram selects minimum and maximum values of aSP data in entered interval. After that it counts segment between min and max and divides him on equal intervals. Quantity of interval are selected by interpreter.

2. On next step subprogram counts the quantity of aSP values in each interval. It completes that by comparing numerical data with boundary values of intervals were selected before.

3. Thus in the following histograms we can see quantity of numerical aSP data on Y axis and intervals were selected by interpreter on X axis (fig. 1).

Fig. 1. aSP interpretation workflow.

On the figure 1 you can see a typical interpretation workflow. So let's study a washover crack fan sub environment. We know that this sub environment beds in depth interval 2770-2772 m from core analysis and takes a aSP curve which consists numerical values with log step 0.2 m on known interval. After that puts this numerical array in Excel subprogram. Subprogram counts the quantity of aSP values in each interval and builds a histogram of aSP values distribution. We know that numerical data aSP can be compared with lithological composition. For example, the interval 0÷0.2 corresponds with clay and interval 0.8÷1 corresponds with coarse-grained sandstone etc [4]. As we know that, the above mentioned histogram can be interpreted as pie chart of percent lithological composition of washover crack fan sub environment (look at figure 1). In the result we can see that the washover crack fan sub environment consists a 16% clay, 39% siltstone, 24% aleurite-clay sandstone, 17% fine-grained sandstone and 4% coarse-grained sandstone. Other Yu₂ sedimentary environments was characterized by interpretation of SP data, look at figure 2 and table 1.

Fig. 2. Sedimentary environments litho-statistical characterization of Yu₂ horizon.

Table. Lithological characterization of Yu₂ horizon sedimentary sub environments based on SP data interpretation.

The second well log method is high frequency electromagnetic logging method (named VIKIZ). Let's learn about deposition environment influences on infiltration thickness, resistivity and it's connections with reservoir properties. Trial interpretation of VIKIZ data was completed in Yu₂ horizon intervals like a interpretation of aSP data. Each sub environments were characterized by next parameters: thickness and resistivity of infiltration, bordering zone and nature bed. The methodology of VIKIZ data interpretation based on EMF PRO software of Institute of Petroleum Geology and Geophysics [5]. On the figure 3 below the text we can see work window with Ust'-Baluckskaya 2003 well wireline data. The interval 2920-2940,5 m was divided on next sub environments: 1 – barred bar; 2 – central part of lagoon; 3 – coastal part of lagoon; 4 – marshes; 5 – fluvial delta branch; 6 – dead channel; 7 – flood plain. After that each interval with VIKIZ data was interpreted and as the result we can see geo-electrical model in right part of work window. This model shows resistivity dependence from distance from borehole and thickness of infiltration zone in each depth interval which corresponds with deposition sub environments. Figure 3 illustrates to us a specific geo-electric models which differ from each other between various sedimentary sub environments.

Fig. 3. High frequency electromagnetic logging data interpretation. EMF PRO work window screenshot. Next log data were illustrated from the left to right part: caliper, SP, GR, NL, VIKIZ, geo-electric model, error (residual).

Another electric and radioactive well log data like a GZ, PZ, LL3/LL7, ML, MLL3/MLL7 was analyzed by simple statistical methods. More specifically it deals with building of statistical curves and characterization of sedimentary environments by mid and modal values of wireline data in Yu₂ horizon depth intervals. These values have a specific meanings which differ between various sedimentary sub environments [2].

In conclusion I'd like to underline next three main points:

1. Detailed statistical analysis of aSP data showed the possibility of sedimentation environments ordering. Each environment of sedimentation has a special lithological model.

2. VIKIZ data investigation showed the deposition environment influences on infiltration thickness, resistivity and it's connections with reservoir properties.

3. Thus we can see a connections between deposition environments and wire line data in research region.

References:

1. Vakulenko, L.G, Yan, P.A. Yu2 petroleumphereness horizon sedimentogenesis and it's meaning in hydrocarbon fields prospecting and exploration, Proceedings of the Tomsk Polytechnic University, V. 316, № 1, 61-66, 2010.

2. Kazanenkov, V.A., Karpov, I.A. Characteristics of Yu₂ horizon sedimentation enviroments in well sections of Latitudinal Priobie by materials of electric and radioactive methods, OIL AND GAS GEOLOGY, 5, 133-139, 2011.

3. Kontorovich, A.E., Kazanenkov, V.A., Vakulenko, L.G. Reservoir sedimentogenesis of Middle-Upper Bathonian layers and it's petroleumphereness in Latitude Ob, Geology and Geophysics, 2, 187-200, 2010.

4. Muromcev V.S. Electrometric geology of sandstones bodies - lithological traps of oil and gas. Leningrad, Nedra, 1984, 260 p.

5. Web page: http://www.vikiz.ru/index.php?lang=en

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