Well Logging and Seismic Data Integration at the Chikanskoye Field

S. A. Skrylev, A.A. Nezhdanov, I.V. Gorlov,
A.A. Doroshenko, N.N. Yaitskiy
TyumenNIIgiprogaz, LLC

The Chikanskoye gas condensate field is located in the central part of Angara-Lena area of Lena-Tunguska petroleum province and is a satellite to the Kovytkinskoye field, which is unique in its reserves. Lithologically screened gas condensate deposits of the Chikanskoye field are confined to sand formations R1 and R2 of the Parfenov horizon of the Verkhnechonskaya suite. Gross thickness of the Parfenov horizon varies from 29.0 m (Chik-174 well) to 50.8 m (YuK-6 well). The thickness of gas saturation at the field varies from 0 m (in wells Chik-2, YuK-4 and Yuk-5) to 13.5 m (No. 24). The porosity factor for gas saturated reservoirs varies from 0.08 to 0.20 unit fractions.

Producing strata are represented by consertal, fine and medium-grain, medium and fine-grain fractions, sometimes with admixture of coarse (up to gravel size) fractions. Sands at the R1 formation have coarser fraction size and more inhomogeneous structures in comparison to R2. Lithological and structural characteristics of the sands, their alteration in sections and GR logs shapes suggest that these deposits were formed in transitional conditions, ie from continental to littoral and shallow marine type [4].

Following the results of granulometric distribution analysis for the R1 reservoir, SNIIGGiMS (Siberian Research Institute of Geology, Geophysics and Mineral Resources) experts distinguished three seperate lateral zones. The first zone, typically featuring coarse- to medium- grain sandstone with a mixture of gravelite fragments, is located in the northern part of the territory (near wells Chik-1, YuK-6 and Kov-31). The second zone (near wells YuK-1, YuK-2 and Chik-174) is established in the western part of the territory, where rock grain sizes are generally smaller. The reservoir here features coarse-medium-fine- grained sandstone with an insignificant gravel mixture. The third zone corresponds to the south-eastern part of the field, where medium- and fine-grained sandstone predominates. Sand units are confined to paleochannel facies in reservoir R2 which formed in alluvial conditions.

An important and peculiar feature of productive Parfenov horizon formations is that along with primary hydromicaceous and chloritic cement, secondary cement is also extensively developed in the sandstone. For most of the R1 reservoir area, the primary cement makes up 3-5%, and up to 7% in the north of the field (near wells YuK-1, YuK-2, Chik-1, Chik-174, Kov-31). The Sandstone of the R2 reservoir has a primary cement content of up to 17%. The cement types are film-pore-filling and crustified. Sometimes, kaolinite is found in the pores. Carbonates and regeneration quartz predominate in secondary cement, comprising over 5% of rock. Dolomite cement is porous, and the porous-basal types reaches up to 5%. Basal cement, up to 45% content, is found in some areas (well YuK-2). Authigenic quartz amounts for up to 5.5% and is omnipresent in the form of regeneration hems, often taking up the central part of the pores. Intensive pyritization zones are found near wells YuK-1 and YuK-2 (totaling up to 6%), where pyrite fills the pores in small accumulations.

Laboratory research shows that an increase in secondary cement content (over 5%) decreases the porosity down to 2% and the permeability to 0.01.10-3 µm2. With that in mind, the most significant decline of reservoir properties is found for rock with an increased regeneration quartz content (over 2%) in the cement, which is typical for the Parfenov horizon sandstone.

Another important feature of the Chikanskoye field is that Parfenov horizon rock is penetrated by a system of vertical and steeply dipping tectonic fractures, most of them open in type. The development of pyrite and anhydrite along the fracture walls is only found on rare occasions in the south-eastern part of the territory. The fracture capacity generally increases the reservoir properties of rock: the porosity is increased insignificantly (only by up to 5%), and permeability may rise by one or two orders of magnitude.

The rock in review is divided into four types by the specific surface of the fractures (Р) [2]: dense (non-fractured) types with P equaling zero; weakly fractured type with P reaching up to 5 m2/m3, mildly fractured type with P values ranging from 5 to 20 m2/m3, and heavily fractured type with P values exceeding 20 m2/m3. Parfenov horizon rock fracturing increases west to east from weakly fractured (Yuk-1) to heavily fractured (Yuk-6). Wells YuK-2, YuK-4, YuK-5 feature mildly fractured rock.

Thus it is visible that aerial reservoir distribution is controlled by not only sedimentation characteristics but also by secondary processes such as minerals cementing the reservoir rock and the formation of leaching zones where the sandstone is decemented. In wells Chik-2, YuK-4 and YuK-5, with a total horizon thickness ranging from 30 to 46 meters, the share of reservoirs is zero. For the area at large, the maximum reservoir share value is also small, comprising at the most one third of the sand formation thickness, such as for instance in well YuK-1, where sandstone is decemented.

The rock features of the horizon in review make it difficult to forecast reservoir development zones based on either geological criteria or rock characteristics from seismic data.

The main factor complicating the creation of a seismic reservoir model at Chikanskoye is the small net productive thicknesses which barely exceeds 10 meters, which falls within one or two wavefield sampling intervals (2 ms at reflected wave propagation speed of up to 4100 m/s).

When resolving inverse amplitude time problems based on thick-layer synthetic wave field production models, selection and tracing of such reservoirs is only possible in certain conditions, and only on a qualitative level. In particular, work [1] demonstrates this possibility for the Parfenov horizon at the Kovytkinskoye field. The work establishes that reflected waves, in 30 ms time intervals covering reflection of Parfenov horizon, significantly stand out by their amplitude and phase characteristics. Particularly, in areas where reservoirs are being developed, the dynamic analysis interval features distinct positive reflections, whereas in non-reservoir zones the wave pattern is a set of low-amplitude negative reflections which are unstable in shape. The data analysis performed in [1] revealed that behavior of amplitude characteristics for reflected waves in reservoir and non-reservoir zones may be accepted as universal criteria for the identification of seismic anomalies related to the gas saturation at Parfenov horizon» ([1], page 59). Basically, the average and maximum amplitude value maps derived from amplitude processing allow reservoir and non-reservoir zones to be identified, however without any estimations of their net productive thicknesses.

These estimations [5] are based on a velocity model of Parfenov horizon, created using PARM-COLLECTOR optimization technology. Here the velocity sections were recalculated into porosity sections, using linear dependencies established in the paper; the resulting sections were then used to derive data on their net productive thicknesses (([5], page 75). This statement reflects the interpretational difficulties of forecasting reservoir thicknesses for the field in review.

First of all, as G.E. Rudenko noted, “due to the poor differentiation of porosity ratios, the reservoirs’ net productive thickness was the main unit used ([5], page 75). On the other hand, the productive thickness is derived («to derive data») by these porosity sections.

Secondly, as it turns out, in some wells “the productive part of the reservoir features a thin interlayering of productive and non-productive sandstone, siltstone and claystone with velocities effectively typical for a reservoir» ([5], page 72). Naturally, in the areas of such depositions net productive thicknesses will be overestimated if derived using this method. This apparently explains the fact that the estimated net productive thickness values near well 1 at Khandinskaya area differ so drastically in wells [1] and [5]. Particularly, [1] (fig. 5, page 59) estimates a large non-reservoir zone near this well, while [5] (fig. 8, page 75) estimates net productive thicknesses here ranging from 15 to 25m, almost reaching the largest recorded for the field.

All these reservoir forecasting difficulties based on CMP and SRM data are also present at Chikanskoye field, where the situation is even more difficult given that the maximum net productive thicknesses are almost three times less than at the Kovytkinskoye. Therefore we can be sure that forecasting net productive reservoir thicknesses based on seismic inversion here is destined for failure.

Due to this fact, this paper reviews a different (compared to [1] and [5]) approach to forecasting lateral reservoir distribution for Parfenov horizon of Chikanskoye field, which is based on the integration of well logging and seismic data using the conversion of seismic traces into pseudologs (CSPTM technology) [3]. This technology allows us to compare the characteristics of seismic data shapes directly against changes in the logs. The seismic data is parameterized by a number of seismic attributes, including Hilbert transformants and other attributes selected by the analyst.

As the technology authors insist, it can serve as a basis «…to forecast a few pseudolog curves (SP, gamma, density, velocity etc.) using the same seismic time sections» ([3], page 5). Therefore, if any logging method allows us to estimate the effective thicknesses, it can also be used to estimate it based on seismic data by computing pseudologs.

At Chikanskoye, acoustic logging is one such a method. That is, in presence of qualitative or indirect evidence of reservoirs, the effective thicknesses here are determined based on porosity factor FP, which, in turn is calculated from DT values and acoustic P-wave propagation time. Dependency of FP on DT is linear, given that as DT increases, so does the FP values. Therefore, increased DT values correspond to enhanced reservoir properties.

However, increased DT values are not enough to identify reservoirs, because the above noted remark «… in presence of qualitative or indirect evidence of reservoirs» here has much significance. The fact is that reservoir intervals with a thin interlayering of non-productive sandstone, siltstone and argillite may have the same propagation velocity characteristics as that of low-clay but low-porosity gas-bearing reservoirs (it is also noted in [5]).

Wells No. 24 and Chick-174 illustrate this case. Figure 1 shows point diagrams of reservoir and non-reservoir distribution based on normalized values of DT and GL (natural rock radioactivity), built by digitizing existing logs with 0.2 m spacing in the Parfenov horizon interval. With that, αDT and αGK values were derived by normalization into minimum and maximum DT and GL values for the Parfenov and Bokhan horizons using these formulae:
αGL=(GL-GLmin)/(GLmax-GLmin), αDT=(DT-DTmin)/(DTmax-DTmin).

It appears that in wells YuK-1 and No.24, low clay reservoirs of R1 reservoir with low (0 to 0.2) αGL values feature increased values of αDT (0.5 to 1.0).
With that, in wells No. 24 and Chik-174, high clay content reservoirs (with αGL ranging 0.2-0.5) have lower αDT values (0.1 to 0.7) which are also found for non-reservoir clay rock (with αGL of 0.6 to 1.0).

Therefore, as seen in the diagrams for wells YuK-1, YuK-4 and No. 24, using αDT value of 0.5, it is possible to identify enhanced reservoirs from non-reservoirs and low porosity reservoirs. Also, in well No. 24 for example, in reservoirs where αDT values exceed 0.5, porosity factor ranges between 0.16 and 0.20, whereas in reservoirs with αDT below 0.5 they range 0.11-0.16 u.f.

The one property of enhanced reservoirs important in forecasting effective reservoir thicknesses based on pseudologs is that they feature increased DT values in comparison to the host rock.

Table 1 shows that the share of enhanced reservoirs in wells at Chikanskoye field generally exceeds 80%, with three wells (No. 24, No. 21 and Chik-174) being an exception. The first of these features approximately equal shares of enhanced high-porosity reservoirs (with FP ranging 0.16-0.20) as well as low-porosity reservoirs (with FP ranging 0.11-0.16). With that, the total thickness of enhanced reservoirs here is one of the largest in comparison to all other wells. It is also seen in Figure 1, where DT values for reservoirs and non-reservoirs are presented.

It is apparent that near these wells, it is impossible to estimate the net productive thicknesses of formations in review, it is only possible to detect total thickness of interlayers with enhanced reservoirs.

Now that we’ve established the possibility of detecting enhanced reservoirs based on αDT logging values, let us expand on the conversion of seismic traces into pseudologs αDTPR using PANGEYA interpretation system (IS).

Overall, forecasting the effective thicknesses of producing formations in this interpretation system is carried out in five stages:
1.    Importing seismic data and computing wave field attributes.
2.    Tying the well log and seismic data.
3.    Selection of most informative seismic attributes for conversion from wave field into pseudologs αDTPR for individual wells and pseudolog computation for all points of seismic profiles (or cubes).
4.    Calculation of the total thicknesses for positive αDTPR anomalies relative to various levels of this parameter’s value in all points of seismic profiles, and tying them to the thickness of enhanced reservoirs in the well.
5.    Building maps of total αDTPR anomalies thicknesses in time scale using pseudolog profiles and recalculating them into enhanced reservoir thicknesses maps.

Stage 1. At this stage time sections and t0 values for horizons M2 (Parfenov reservoirs top) and MPb (Parfenov reservoirs bottom), which were earlier correlated using SeisWork (Landmark) interpretation system, were loaded into the IS. Then seismic data for 13 ms interval below M2 horizon was cropped because this is the interval containing Parfenov horizon reservoir’s reflections for this territory. Then based on these curtailed time sections, a range of seismic attributes was computed with time field sampling at 0.5 ms intervals.

Stage 2. Logging data from exploration and prospecting wells were uploaded into IS PANGEYA and seismic information was tied with the well log data. We should note that during conversion of the well logs into time scale, the initial data was smoothed in the same manner as the seismic attributes at 0.5 ms intervals. With that, the αDT value range narrowed down. Therefore, in well YuK-1, where enhanced reservoirs are identified, initial αDT values varied from zero to one (Fig. 1), whereas the values smoothed by sampling intervals varied from 0.38 to 0.69 (Fig. 2). At the same time, in well YuK-4, where no reservoirs are identified, initial αDT values varied from zero to one (Fig. 1), whereas the values smoothed by sampling interval varied from 0.1 to 0.44 (Fig. 2).

Stage 3. The selection of the most informative seismic attributes for conversion of wave field into αDTPR pseudologs was done by conducting a full search of seismic attributes and selecting those which provide maximum multiple correlation coefficient relative to αDT for each of the wells. It was then determined that the best relation of αDT with the dynamic parameters is seen near wells YuK-1 and YuK-4 (Fig. 2). With that, the most informative attribute was instantaneous amplitude (Ains), which features a high correlation coefficient of 0.84 with αDT (Fig. 3A).

However, as seen in Figures 2A, 2C and 3A, the pseudologs are overly smoothed in comparison to the well logs. With that, low αDT values are overestimated (from 0.1 to 0.35), and high αDT values are underestimated (from 0.69 to 0.55). This fact motivated a search for other attributes, which in combination with Ains would improve the quality of pseudologs. Pseudoacoustic transformation (PAT) and the result of wave field homomorphic deconvolution (DC) proved to be such parameters.

Using a set of attributes Ains, PAT and DC, the quality of αDT forecast for high values range (fig. 2B) had improved. Here, maximum deviations of αDT from αDTPR are 0.1 and not 0.15, i.e. 1.5 times less than that for connection with only Ains. Naturally, creating pseudologs using a set of three parameters is more preferable that Ains only, although multiple correlation coefficient increased insignificantly, from 0.84 to 0.85 (Fig. 3).

The final operation of stage 3 is recalculation of seismic attributes Ains, PAT and DC sections into pseudolog section αDTPR, one of which is shown in Fig. 4. It intersects well YuK-1, where enhanced reservoirs comprise 87.8% (Table 1, Fig. 1) based on logging data.

Judging by the color scale, forecasted αDTPR values within this section vary between 0.3 and 0.65. With that, the widest area of increased αDTPR values is found specifically near YuK-1 well.

Stage 4. An estimation of the share of increased αDTPR values for each of the pseudolog sections was performed by calculating total thicknesses (in time scale) of positive anomalies of this parameter at its various levels hαDTPRlevel for all points of seismic profiles. We used levels 0.3, 0.4, 0.5, 0.6 in this work. When tying total thicknesses of positive anomalies calculated in well locations to the thicknesses of enhanced reservoirs (hу) in corresponding wells, it was determined that the largest values of paired correlation coefficients (over 0.75) were found for relations of hу with hαDTPR0.5¬, whether forecasting αDTPR was based on one seismic attribute (Ains) or a set of them (Ains, PAT and DC).

Considering the fact that using a set of attributes Ains, PAT and DC improves the quality of hαDTпр0,5(Амгн, ПАК, ДК)DT forecast specifically for high value ranges (fig. 2B), the following dependency was further used to build forecast maps of enhanced reservoirs thicknesses:

hу=0.98.hαDTPR0.5(Аins, PAT, DC) + 0.39   (1),

which features correlation coefficient r=0.86 (fig. 5).

Stage 5. Using PANGEYA, a map of total thicknesses for positive anomalies hαDTPR0.5(Аins, PAT, DC) was created. Then, using dependency (1), this map was recalculated into the forecast map of enhanced reservoirs thicknesses.

Figure 6 shows such forecasted hy values map for Chikanskoye license area and its nearest surroundings. It appears that increased enhanced reservoirs’ thicknesses are distributed across the field quite sporadically. We should note that this forecast map was created in 2006 while preparing submission of the Parfenov horizon reserves calculation to GKZ, which served as the basis for separation of South-Kovytkinskoye gas field into an individual Chikanskoye gas condensate field. By that time, seven wells were drilled at the field (Kov-31, Chik-1, Chik-2, Chik-174, YuK-1, YuK-2, YuK 4). Data from these wells was used to derive dependency (1).

Between 2007-2011, four new exploration wells (YuK-5, YuK-6, No. 21, No. 24) were drilled at Chikanskoye field. As seen in Figure 7, these wells confirmed our forecast. In particular, we forecasted an area of increased hу¬ values (over 6.0 m) near well No. 24, and 7.3 m of enhanced reservoirs were identified by well logging data. For the rest of the wells, values near three meters were forecasted and the thickness detected ranged between 3.0-4.0 m.

It should be noted that the forecast map that was created could lead to a conclusion that there are high prospects of gas bearing capacity in untapped south-western part of Chikanskoye license area, where even thicker enhanced reservoirs are estimated than those at the drilled territory.

Conclusions:
The method of integrating well logging and seismic data to forecast the thickness of reservoirs based on the conversion of seismic traces into pseudologs using IS PANGEYA made it possible to give reliable estimations of gas bearing prospects for the terrigenous Vendian deposits at at the Chikanskoye field.

The enhanced reservoirs thicknesses map may be used to substantiate the location of new exploration wells.

Literature:
1.    Baryshev L.A., Khokhlov G.A. Complex interpretation of seismic and well logging data based on physical-geological model / Seismic exploration technologies. Moscow, 2006. No. 3. P. 55-60.
2.    Vorobyov V.N., Melnikov N.V. Fractured reservoir investigation methods. // Reservoir investigation methods and reservoir rock properties in petroleum regions. Works of SNIIGGiMS. Novosibirsk, 1966. Issue 33. P. 5-28.
3.    Conversion of seismic data into pseudologs instead of acoustic inversion. / Kolesov V.V., Smirnov O.A., Zakharova G.A., Nedosekin A.S. // Efficiency of using geophysical data in hydrocarbon reservoir modeling for estimation of oil and gas reserves. / Material of geophysical scientific and practical conference TyumenOEAGO, November 29-30, 2006, Tyumen, 2006. P. 3-5.
4.    Structural characteristics and reservoir properties of Vendian oil and gas reservoir in the southern part of Siberian platform. / K.S. Kondrina, L.S. Chernova, T.N. Dergachova // Scientific work collection of SNIIGGiMS. Lithology of oil and gas reservoirs in Mesozoic and Paleozoic depositions of Siberia. Novosibirsk: SNIIGGiMS, 1982. P. 15-28.
5.    Rudenko G.E. Again on the results of research at Kovytkinskoye field using PARM-COLLECTOR optimization technology and on possibility of identification and tracing of thin beds // Seismic exploration technologies. Moscow, 2006. No. 3 P. 69-85.