First Break - Volume 24, Issue 10, 2006

This concise overview of Russian oil and gas policy and activities comes from an independent research report to the US Congress by Bernard A. Gelb, specialist in industry economics, resources, science, and industry division, Congressional Research Service, the Library of Congress. This is a slightly abridged, unreferenced version, but the specific discussion of US interests has been left unedited. All photos in this article are from the major ongoing Sakhalin-1 project in the Russian Far East. The Russian Federation is a major player in world energy markets. It has more proven natural gas reserves than any other country and is among the top 10 countries in proven oil reserves. It is the world’s largest exporter of natural gas, the second largest oil producer and exporter, and the third largest energy consumer. Oil and gas reserves and production Most of Russia’s 60-72 billion barrels of proven oil reserves are located in Western Siberia, between the Ural Mountains and the Central Siberian Plateau. This ample endowment made the Soviet Union a major world oil producer in the 1980s, reaching production of 12.5 million barrels per day (bbl/d) in 1988. Roughly 25% of Russia’s oil reserves and 6% of its gas reserves are on Sakhalin Island in the far eastern region of the country, just north of Japan. Russian oil production, which had begun to decline before the Soviet Union dissolved in 1991, fell more steeply afterward - to less than six million bbl/d in 1997 and 1998. State-mandated production surges had accelerated depletion of the large Western Siberian fields and the Soviet central planning system collapsed. Russian oil output started to recover in 1999. Many analysts attribute this to privatization of the industry, which clarified incentives and shifted activity to less expensive production. Increases in world oil prices, application of technology that was standard practice in the West, and rejuvenation of old oil fields helped boost output. After-effects of the 1998 financial crisis and subsequent devaluation of the ruble may well have contributed. After reaching about nine million bbl/d in 2004 depending upon the estimating source, Russian oil production continued to rise in 2005, but only slightly. Several consortia have begun producing and exporting oil (mainly to East Asia at present) from Sakhalin island. They also plan to export gas to the US via pipelines to the Siberian mainland and then from liquefied natural gas (LNG) terminals. With about 1700 trillion cu ft (tcf), Russia has the world’s largest natural gas reserves. In 2004, it was the world’s largest natural gas producer and the world’s largest exporter. However, its natural gas industry has not done as well as its oil industry in recent years, as production has increased only a little and exports only have re-attained their level of the late 1990s. Growth of Russia’s natural gas sector has been impaired by ageing fields, near monopolistic domination over the industry by Gazprom (with substantial government holdings), state regulation, and insufficient export pipelines. Gazprom, Russia’s 51%-owned state-run natural gas monopoly, holds more than one-fourth of the world’s natural gas reserves, produces nearly 90% of Russia’s natural gas, and operates the country’s natural gas pipeline network. The company’s tax payments account for around 25% of Russian federal tax revenues. Gazprom is heavily regulated, however. By law, it must supply the natural gas used to heat and power Russia’s domestic market at government-regulated below-mar-ket prices. Potential growth of both oil and natural gas production in Russia is limited by the lack of full introduction of the most modern western oil and gas exploration, development, and production technology.

Prof. N.A. Savostyanov, president, Eurasian Geophysical Society and longtime luminary in the Russian geoscience community, offers an analysis of some of the key trends in the development of the Russian market for geophysical services in relation to the oil and gas E&P business. At present, the oil and gas industry forms the basis of the Russian economy. High and ever-growing world prices for oil and gas have been providing huge financial inflows into the both Federal Budget and the government’s so-called Stabilization Fund. The worry is that the current Russian Administration is being short-sighted by stimulating oil production but neglecting the appropriate reserves growth. At the current oil production of 460-480 million topy (tons of oil per year), oil reserves growth is about 220-250 million topy. At present as few as two or three moderate-size new fields are put on stream annually. Reserves growth is mainly achieved via reserves reassessment in the mature areas. E&P activities, including geophysical exploration, are carried out by local oil companies on quite a modest scale. With the cancellation of Mineral Resources Base Reimbursement (MRBR) rates, oil companies considerably reduced their investments in E&P. As a result, according to BP data, Russia’s rank in proved oil reserves dropped down below Saudi Arabia, Iran, Iraq, Kuwait, UAE, and Venezuela. In this situation, Russian geophysical companies have managed to keep their spirits up, for example, by offering their services to local oil producers for such tasks as precise field modelling. In the Russian context, the widespread term ‘geophysical exploration and prospecting’ should be changed to ‘geophysical methods of exploration, prospecting, and field development’. That’s what can be surmized from local geophysical contractors’ operations in the year 2005. In total, the volume of geophysical E&P for oil and gas in Russia last year amounted to 26-28 billion rubles (about $1 billion). As compared to 2004, the number of seismic crews (now over 150) has grown, as well as the number of well logging and perforation crews (now over 2500). This year, the amount of geophysical operations should exceed last year’s figures by 10-12%. This has given rise to a number of geophysical and service company managers complaining about the shortage of skilled engineering personnel and especially field technicians.

Erling M. Frantzen and Kjell E. Trommestad, TGS-NOPEC Geophysical Company, explain how a modern marine 2D seismic data being acquired in a multi-client survey in the Sea of Okhotsk is being combined with gravity and magnetic data to challenge previous ideas about the limited petroleum prospectivity offshore Far East Russia. Numerous onshore discoveries beginning with the revelation of the Okha field on Sakhalin Island in 1923 have indicated that active petroleum systems underlie a large part of the Sea of Okhotsk, a mostly unexplored epicontinental sea covering a 900 km x 1200-km area in the Russian Far East. When the Tockinsky 1 well encountered a significant oil show at a depth of 800 m on the Kamchatka peninsula in 1932, it touched off more than three decades of exploration in the region and by 1971, 35 onshore oil, gas, and condensate discoveries had been reported. Offshore exploration activity in the Sea of Okhotsk was initiated in the early 1970s and for the most part has been limited to areas offshore Sakhalin Island. Significant oil and gas discoveries offshore Sakhalin, including Odoptu in 1977, Chaivo in 1979, Lunskoye in 1984, Piltun-Astokhskoye in 1986, and Arkutun-Dagi in 1989, established offshore Sakhalin as a world-class hydrocarbon province with reserves exceeding 5.5 billion barrels of oil and 35 trillionft3 of gas. Total reserves in place in the area were estimated to be more than 90 billion barrels of oil equivalent in a narrow corridor east of the Sakhalin Island, where all of the above discoveries are located. Several international exploration companies joined the hunt in the late 1980s and early 1990s, including BP, ChevronTexaco, ExxonMobil, Rosneft, Shell, and others. A joint venture of BP and Rosneft announced in October 2004 that the Pela Lache 1 wildcat well had encountered significant volumes of oil and gas in several high-quality sandstone reservoirs on the 6000 km2 Kaigansky-Vasukansky exploration licence block about 40 km northeast of Sakhalin, marking the first successful drilling activity outside the established fairway offshore Sakhalin in what is considered the modern era of oil and gas exploration. BP and Rosneft announced in October 2005 that a second exploratory well, Udachnaya 1, had encountered hydrocarbons in three zones at a surface location about 15 km west of the Pela Lache 1 wildcat.

Christophe Ribeiro and Colin MacBeth present a new petro-elastic-based approach to independently estimate reservoir pressure and saturation from 4D seismic data obtained at BP’s Foinaven field, west of Shetland. In the last decade, the use of seismic monitoring (4D) has greatly increased, and oil companies have shown their commitment to this technology (Marsh et al., 2003; De Waal and Calvert, 2003), as confirmed by its more systematic application. Time-lapse seismic is now an integrated part of reservoir management strategy, and has proved its ability to improve reservoir understanding. In the past, the applications of seismic monitoring were predominantly focused on the tracking of fluid contacts and movements (i.e. gas-cap expansion, water sweep); discrimination between lithology and fluid effects; and identification of pressure compartment and reservoir connectivity. The growth of seismic reservoir monitoring has brought new technical challenges to the industry, in order to improve and enlarge the existing portfolio of this technology. In fact, the advance in acquisition (i.e. steerable streamers, full-wave field recording devices, permanent sensors), survey design (i.e. 4D dedicated acquisition), seismic processing (i.e. specific 4D workflow) and integration between contractors and oil companies (i.e. in-house processing and R&D teams) have improved the repeatability and quality of the data, and paved the way for new applications. Research has now moved towards the more quantitative aspects of 4D, attempting to estimate hydrocarbon-production-related changes directly from seismic data. However, in most cases, seismic amplitude changes are not only due to variation in fluid saturation, pore pressure, or even compaction, but to a combination of these effects. The goal of distinguishing between pore pressure and fluid saturation effects is a challenge in quantitative time-lapse seismic interpretation, and has been the subject of many papers. The separation of pressure and saturation effects has mainly been tackled by means of rock-physics-based techniques (Brevik, 1999; Tura and Lumley, 1999; Landrø, 2001), and recently engineering approaches using production, pressure and PVT data, have been developed (He et al., 2004; MacBeth et al., 2004). The independent estimation of pore pressure and fluid saturation, which are also two products from the reservoir flow simulation, allows a direct comparison between attributes derived from the seismic and engineering domains. These production attributes could be used in a 4D seismic history matching workflow in order to directly constrain the predictions from the fluid flow simulator with the seismically derived dynamic properties (Gosselin and Menezes, 2006; Stephen and MacBeth, 2006). Modifications of the reservoir model could also be made to improve the agreement between the flow simulation results and the seismically derived production estimates, and ultimately to increase reservoir performance and oil recovery.

Gerard de Jager and Raymond J.W. Pols of JOA Oil & Gas, based in The Netherlands, describe their company’s work in developing a software application suite and reservoir modelling methodology to define, create, and easily maintain up-to-date static and dynamic reservoir simulation models. The Windows .NET based tool is aimed at resolving some of the key bottlenecks in today’s multi-disciplinary reservoir modelling workflow. The main driver for new solutions is the shifting E&P market where production of complex fields and heavy hydrocarbons has become increasingly important. This gives rise to the need for up to date reservoir models with a fast turn around time. The vision of building better and higher quality reservoir models can be realized by no longer focusing on ‘best in class’ for individual parts of the static and dynamic workflow, and instead generating better insight on the total integrated reservoir model, referred to as ‘three models in one’ - the geophysical model, the geological model, and the reservoir simulation model. This is what we have tried to achieve with the JOA Jewel Suite. Modelling complex geology A number of the current industry modelling packages try to solve the 3D gridding of faulted reservoir models by aligning the pillars to the fault in two different ways. The disadvantage of the method shown in Figure 2a is that the lateral sizes of the cells (grid blocks) vary too strongly so that it can only deal with relatively simple fault topologies. In most cases this produces almost vertical branch lines. As a result more complicated fault topologies like y-faults, thrust faults, and x-faults cannot be honoured. The second method shown in Figure 2b can handle both vertical branch lines as well as horizontal branch lines. For horizontal branch lines it creates collapsed cells along the fault surface on which the pillars truncate. However, it creates distorted cells when the fault on which the pillars truncate ends somewhere in the middle of the model. Figure 3a shows an example where Fault A ends and the distorted cells occur because there is no surface to collapse them on. The conclusion of this problem analysis is that the pillar grids based on pillar truncations can handle simple shape fault topologies only. They cannot deal with the more complex topologies of faults like those shown in Figure 3b. The area between the faults indicated with the X cannot be gridded, because the pillars have to reach the top or base of the reservoir. Furthermore, for other complicated geological events, such as salt bags, unconformities, and erosions, pull up of the K-Layers can occur, as shown in Figure 3c, because the pillars are not aligned with the erosion surface. This will also result in distorted cell shapes which will influence the behaviour of the geological and reservoir simulation model in the rest of the workflow.

Hüseyin Özdemir, Jesper Wissendorf Hansen, and Emma Tyler provide an example of mapping of oil-water contacts where pre-stack AVO inversion to rock physics parameters provided improved delineation whilst post stack inversion procedures including elastic inversion gave inconclusive results. A Paleocene field was discovered in year 2000 in the Danish North Sea on the flank of a salt dome. The field was further appraised by a sidetrack discovery well and two subsequent vertical wells through years 2000 and 2001. Full logging suites were acquired in all wells including shear sonic data. VSP data were acquired in the three vertical wells. The wells were positioned using a 3D seismic data set, which was not specifically processed for Paleocene targets and no AVO processing was performed. Hence it was decided to re-process the data for AVO analysis and as part of this to perform forward modelling and fluid substitution in order to calibrate the seismic anomalies to the results of the forward modelled well data and use the results as an exploration/appraisal tool. The main exploration and development challenge is to find a seismic attribute that can differentiate between hydrocarbon and water zones. Conventional and pre-stack AVA inversion analysis was also made for lithological and pore fluid determinations. The latter analysis resulted in reservoir rock properties such as Poisson’s ratio (m), hl and +l. h and + are the Lamé constants and l is the density. Other combined and weighted attributes have been computed for more accurate pore fill identification. This paper is based on our EAGE presentation (Özdemir, Hansen, and Robinson/Tyler, 2003), which also includes a brief discussion of pre-stack inversion to acoustic impedance (AI), shear impedance (SI), and density using full Aki-Richards equations (Aki and Richards, 1980; Stewart 1990; Fatti, Smith, Vail, Strauss and Levitt, 1994; Larsen, Margrave, and Lu, 1999). This inversion methodology was first introduced by Özdemir, Ronen, Olofsson, Goodway, and Young (2001) for the joint inversion of multicomponent seismic p-wave and converted wave data.

Darrel Norman, GeoKnowledge USA, suggests that developments in exploration technology and assessment methodologies provide new opportunities and challenges. The modern probabilistic prospect assessment must incorporate multiple zones, risk dependencies and volume correlations, discrete contact scenarios, and contact uncertainty integrated across depth-volume functions in order to honour the uncertainties defined by the evaluation. The modern oil exploration business is a business of decisions. The managers of exploration efforts, from the largest majors down to the smallest independents, are continually choosing between alternatives. Where should we deploy our people? What plays should we evaluate? Should we acquire acreage? Which opportunity should we drill? The decisions are usually made based on the evaluation of a prospect or a group of prospects. Even pure frontier plays are judged based on the potential of real or postulated leads. The prospect evaluation attempts to answer the fundamental question of oil and gas exploration: ‘What is the chance that this prospect will be a commercial success?’ To answer that question, we must describe the range of possible recoverable volumes, and the chances of finding those volumes. The process of estimating the range of potential volumes and their associated probabilities is called probabilistic prospect assessment. It involves quantifying geologic risks and uncertainties. Risk and uncertainty are largely matters of perception. They cannot be measured or calculated directly. Our perception of a prospect’s risk and uncertainty is a function of the geoscientist’s evaluation and the confidence that we have in the evaluation. ‘Evaluation’ should not imply a single map or model. The geoscientist’s evaluation of a prospect should include full disclosure of all possible outcomes and their relative likelihood. The systematic quantification of risk and uncertainty is a numeric expression of the evaluation. Over the past 15 years our ability to quantify the geologic risks and uncertainties associated with the typical exploration prospect has improved tremendously. The primary source of the improvement has been the use of 3D seismic and the detailed evaluations that are now possible prior to testing a prospect. In some trends, 3D seismic provides a level of detail that previously could be obtained only with dense well control. Our ability to make detailed evaluations of undrilled prospects has necessitated a change in our probabilistic assessment methods. As our descriptions of the untested prospect’s risk and uncertainties become more detailed, our methods for quantifying those perceptions must also become more detailed. Otherwise, the probabilistic assessment will not support the geologic evaluation (and vice versa). Modern geologic evaluations deserve modern assessments of uncertainty and risk.

Anatoli A. Tikhonov (PetroAlliance), Ludmila A. Plekhodkina (Lukoil), and Enru Liu (British Geological Survey) in this technical article describe a method for the delineation of fracture-induced azimuthal anisotropic intervals in the subsurface supported by a case study with Russian data. Traditional seismic techniques of examining fracture-induced seismic anisotropy in a borehole utilize either walkaround observations of direct compressional waves (Horne 2003; Horne et al., 2002; Thompson et al., 2002), observations of vertical shear-waves with different polarizations (Brodov et al., 1991, Liu, Crampin and Queen, 1991; Winterstein, Ge, and Meadows 2001), or converted PS waves in offset VSP data (Horne et al., 2002). The walkaround VSP data allows the construction of the P-wave velocities (Vp) indicatrix. The observations of vertical shear-waves are based on shear-wave splitting or birefringence measurements. The elastic moduli are usually calculated from the kinematic parameters of seismic waves assuming the model of transversally anisotropic media with a horizontal symmetry axis. For more complicated models (for example, thin-layer section with vertical fractures leading to orthorhombic model) the 3C multi-azimuth, multi-offset VSP is used (Dewangan and Grechka, 2002). Our goal is to extract information about seismic anisotropy due to the vertical fracturing from routinely recorded offset VSP data obtained with non-directional or explosive sources. We attempt to answer the following two questions: (1) Is it possible to control the polarization of converted PS waves in order to obtain pure S1 and S2 modes? and (2), How to remove the influence of offset PS ray paths in order to estimate time delay between S1 and S2 waves? The idea of controlling the polarizations of converted down-going PS waves is based on the use of linear combinations of wavefields recorded along a vertical seismic profile from pairs of nondirectional sources located in different directions (Figure 1). We assume that the shear wave polarizations after P to S mode conversion at a given interface in horizontally layered isotropic media is contained in a vertical ray plane passing through the sources and receivers. The original technique, which uses the rotation of shear-wave polarizations proposed by Brodov et al. (1991), assumes that two different shear modes propagate along the same ray paths. However, surface seismic data use the rotation method to detect the directions of anisotropy symmetry axes using data obtained along different ray paths. This technique assumes that NMO corrections reduce different ray trajectories to the same vertical ray. We use a ray-tracing technique to remove the influence of PS ray geometry on arrival times and to reduce to vertical profile the wavefield registered from offset VSP. The initial kinematic model is built by the inversion of direct P and converted PS wave travel-times. We correct the 2D kinematic model after imaging of depth migrated seismic section.