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Seismic processing and interpretation
Seismic-Based Gas-Water-Contact, Porosity, and Permeabilities for Condensate Reservoirs: Tested and Corrected by History Matching
Kasap, Ekrem (Schlumberger Oilfield Services) | Lumay, Viloria (Schlumberger Oilfield Services) | Xue, Fangian J. (Schlumberger Oilfield Services) | Corbett, Chip (Schlumberger Oilfield Services) | Nishikiori, Nobuo (Arabian Oil Co.) | Tomonori, Shibata (Arabian Oil Co.)
Abstract Seismic attributes are an essential source of information on lateral variation in rock properties. Quantitative use of seismic attributes in predictive models is challenging, because the seismic data are of limited resolution and influenced by rock mechanical properties, porosity, pore fluid type, bed thickness, and dip angle. An integrated study was conducted to optimize the coupled gas production from Fields A and B, offshore Gulf of Mexico. Field B formation characterization results are discussed here. Structural static models were constructed from depth-converted seismic horizon interpretations. The models were porosity populated by integrating seismic amplitude-based areal porosity heterogeneity with well log and core data using geostatistical techniques. All other necessary petrophysical parameters were correlated to porosity. Static models were then converted to numerical models, which were used to predict historical data. Field B has Upper, Middle, and Lower sands, which were produced from dual completions. The Upper sand is thick enough to be resolved on the seismic data. The gas-water contact (GWC) was reliably determined and verified by history matching. The matching production data, tubing head pressure (THP), and static reservoir pressure revealed, however, that seismic amplitude overestimates porosity above the GWC, and underestimates it in no-gas aquifer zones. This effect is amplified in permeability because of its exponential relationship to porosity. THPs and well tests were used to correct for permeability. The Lower sand reservoir quality deteriorates quickly in most parts of the reservoir, and the seismic resolution was less than desired. Here, in addition to pore volume and permeability, the GWC needed corrections to match historical data. From these findings, we concluded that seismic-based areal heterogeneity is a valuable source of data for predictive reservoir models when supported by the dynamic production and well test data. Seismic Interpretation Horizons and Faults Area 3-D seismic data were interpreted for structural framework and attribute analysis. Synthetic wellbore seismograms were overlaid on phase-matched (gross-shifted and phase-rotated) seismic data to conform the geological correlations (Fig. 1). Field B formations can be subdivided into Upper, Middle, and Lower sands. The Upper sand is in a prograding complex, with good lateral continuity. The Middle and Lower sands belong to a slope fan complex and have relatively poor continuity (Fig. 2). The five horizons corresponding to the tops of the Upper and Lower sands and the center of the Middle sand were interpreted at the troughs on the seismic traces. The bases of the Upper and Lower sands correspond to the peaks (Figs. 1โ2). Fault surfaces were constructed from seismically interpreted fault traces. A fault framework was built by reconstructing the faulting in the order it was predicted to have occurred in geologic time. The final framework used to generate the necessary geologic surfaces contained five major faults. Figs. 3โ5 show major fault lines and two major fault blocks: the Main Block and Block B. Horizons and Faults Area 3-D seismic data were interpreted for structural framework and attribute analysis. Synthetic wellbore seismograms were overlaid on phase-matched (gross-shifted and phase-rotated) seismic data to conform the geological correlations (Fig. 1). Field B formations can be subdivided into Upper, Middle, and Lower sands. The Upper sand is in a prograding complex, with good lateral continuity. The Middle and Lower sands belong to a slope fan complex and have relatively poor continuity (Fig. 2). The five horizons corresponding to the tops of the Upper and Lower sands and the center of the Middle sand were interpreted at the troughs on the seismic traces. The bases of the Upper and Lower sands correspond to the peaks (Figs. 1โ2). Fault surfaces were constructed from seismically interpreted fault traces. A fault framework was built by reconstructing the faulting in the order it was predicted to have occurred in geologic time. The final framework used to generate the necessary geologic surfaces contained five major faults. Figs. 3โ5 show major fault lines and two major fault blocks: the Main Block and Block B.
- Geology > Geological Subdiscipline (1.00)
- Geology > Structural Geology > Fault (0.94)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.49)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
Abstract This contribution demonstrates how the seismic technique can be a valuable tool to improve the accuracy of simulation predictions and optimize the efficiency of hydrocarbon production. We illustrate how time-lapse 3D (or 4D) seismic can be used to monitor the movement of oil-water contacts and map changes in saturation throughout a reservoir over discrete intervals in time. Quantitative data can be extracted from the monitoring quantity, i. e. either seismic amplitude data or acoustic impedance data, depending on whether the flow regime being considered is segregated or diffusive. For segregated flow conditions, 4D seismic can be used to monitor the movement of the oil-water contact (OWC) in time and space. It can also clearly identify undrained compartments of a producing reservoir and be a valuable guide for planning new wells. Potential problems such as coning can be โseenโ prior to water breakthrough and be prevented by controlling production and injection rates. For diffusive flow, where there is no explicit OWC, acoustic impedances can be used to map changes in saturation and evaluate areal sweep efficiency. Two examples are discussed to illustrate 4D seismic monitoring of segregated and diffusive flow respectively. The first example is from a sandstone reservoir at 1600m depth in the North Sea, with porosity of 27% and permeability of 4 Darcy. The second example is of an inverted 5-spot water flood pilot project, where 4D seismic was used over a time span of 6 months for a 90m thick carbonate reservoir at 2000m depth, with porosity of 21% and permeability of 31mD. We will also show results of two physical fluid-flow models where air and kerosene were injected and monitored with 4D acoustics under controlled conditions. These results serve as an impressive demonstration of the ultimate resolving power of the seismic technique in measuring actual flow in a reservoir. Introduction After a brief review of the general hydrogeological conditions within a reservoir, the types of reservoirs and the corresponding recovery methods are briefly reviewed. Based on this summary, two basic types of 4-D seismic applications are discussed: segregated flow-methods and diffusive flow-methods. The underlying acquisition geometry for the two seismic surveys (which form a 4-D data base) is discussed. It is pointed out, that the acquisition geometry for both surveys should be the same. One basic reason for this constraint is the fact that reflectivity depends on offset, and different geometries lead to different sampling schemes in the reflectivity-offset domain, and consequently to different reflectivities for the same interface. Subsequent differencing after processing would then result in a lot of artificial noise due of this effect. Different acquisition geometries would lead to an avoidable and unacceptable increase of the noise in the differenced volume. In the case where legacy data have to be used as the first survey, one has to try to match the acqisition geometries as well as possible. This is not only important in terms of far offsets, but also in terms of near offsets. As an example, by going from a single cable/single source to a N-cables/dual source-configuration, one will also have to apply an inner trace mute. If this is not done, the noise in the differenced volume will go up and may even ruin the dataset for a proper 4-D application.
- North America > United States (1.00)
- Oceania > Australia > Western Australia (0.28)
- Europe > United Kingdom > North Sea (0.24)
- (3 more...)
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.69)
- Geophysics > Time-Lapse Surveying > Time-Lapse Seismic Surveying (1.00)
- Geophysics > Seismic Surveying (1.00)
- Europe > Norway > Norwegian Sea > Halten Terrace > Block 6407/9 > Draugen Field > Rogn Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > Block 6407/9 > Draugen Field > Garn Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > Block 6407/12 > Draugen Field > Rogn Formation (0.99)
- (5 more...)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Four-dimensional and four-component seismic (1.00)
- (2 more...)
ABSTRACT The purpose of this paper is to show how to estimate geomechanical parameters for improved recovery and coalbed methane production processes using an integrated flow model. An integrated flow model combines a petrophysical model with a traditional flow simulator. The usefulness of reservoir geophysical information from an integrated flow model is discussed for the following scenarios: forecasting the reservoir geophysical response of CO2 injection in a mature oil field; estimating subsidence during depletion of an oil reservoir with a gas cap; and predicting the change in geomechanical properties during the life of a coalbed methane reservoir. INTRODUCTION The purpose of this paper is to show how to estimate geomechanical parameters for improved recovery and coalbed methane production processes using an integrated flow model. An integrated flow model combines a petrophysical model with a traditional flow simulator. The integrated flow model was originally devised to assist in the design and analysis of timelapse seismology because the petrophysical model can calculate such reservoir geophysical attributes as acoustic impedance, reflection coefficient, shear velocity, and compressional velocity. We have found that integrated flow models have other important uses. Using the integrated flow model, we can readily calculate such geomechanical properties as Poisson's ratio, Young's modulus, and uniaxial compaction. These properties are calculated from a minimal input data set and are provided throughout the life of the reservoir. They give us insight into the behavior of the structure of the reservoir and the impact of structural changes on fluid flow. The usefulness of reservoir geophysical information from an integrated flow model is discussed for the following scenarios: forecasting the reservoir geophysical response of CO2 injection using advanced well technology in a mature oil field; estimating subsidence during depletion of an oil reservoir with a gas cap; and estimating the change in geomechanical properties during the life of a coalbed methane reservoir. The petrophysical algorithm used in the integrated flow model is described first. PETROPHYSICAL ALGORITHM A prototype integrated flow model (IFLO) based on a widely used petrophysical model has been developed and applied to a range of reservoir systems. The petrophysical model must be able to calculate reservoir geophysical attributes that can be compared with seismic velocity and impedance measurements. The algorithm for calculating seismic velocities is a rock physics model. We refer to the algorithm used in the integrated flow model as a petrophysical algorithm because of its dependence on rock physics properties and petroleum fluid properties. Bulk density for a porous rock with porosity f is given by ?B=(1-)?m+f?f. Rock matrix density ?m and initial porosity are user-specified input data. Porosity depends on fluid pressure P and porosity compressibility cf =(1/f) (?f / ?P)T at constant temperature T. Oil, water and gas densities (? o, ?w, ? g) and saturations (S o,Sw,Sg) are needed to calculate fluid density ?f. Phase densities are obtained from the fluid properties model in the traditional flow simulator, and saturation distributions are obtained as solutions of the flow equations.
- North America > United States (1.00)
- North America > Canada > Saskatchewan (0.47)
- North America > Canada > Saskatchewan > Williston Basin > Weyburn Field > Mission Canyon Formation (0.99)
- North America > Canada > Saskatchewan > Williston Basin > Weyburn Field > Madison Formation (0.99)
- North America > Canada > Saskatchewan > Williston Basin > Weyburn Field > Forbisher Formation (0.99)
- (5 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (1.00)
IOR: The Brazilian Perspective
Shecaira, Farid S. (Petrobras Research Center) | Branco, Celso C.M. (Petrobras Research Center) | de Souza, Antonio L.S. (Petrobras Research Center) | Pinto, Antonio Carlos C. (Petrobras S.A.) | de Holleben, Carlos R.C. (Petrobras S.A.) | Johann, Paulo R.S. (Petrobras S.A.)
Abstract The intent of this paper is to summarize the IOR evolution in Brazil and its perspectives for the future. First of all, a brief history of the oil industry in Brazil is presented, with special prominence to the establishment of the oil industry monopoly in 1953. Next, the main IOR efforts carried out by the industry are reported, beginning in the sixties with water injection in onshore fields, and subsequently with the use of several tertiary recovery methods. The gradual increase in oil production in offshore areas, as from the late seventies, changed the IOR panorama. Presently, water injection is the main IOR activity in offshore fields, which account for approximately 74% of the Brazilian daily oil output. The discovery of oil in deeper waters, as well as more recent facts, such as the enactment of the law breaking, in 1997, the mentioned Brazilian oil industry monopoly, added new components to the industry organization. These new facts can be of major importance in the future development of IOR activities in Brazil A brief history of the oil production in Brazil. Overview of present oil production activities The first commercial oil strike in Brazil occurred in 1939, in the Rec ncavo basin, in the Northeast of the country (see Figure 1). In 1953, a constitutional amendment created the state oil monopoly, comprising the activities of transportation, refining, exploration and production. Petrobras, the Brazilian state owned oil company, was founded in 1954, to be the sole executor of the monopoly on the protected activities and to compete with major companies in the distribution and retail segments, not monopolized but heavily regulated. The oil production increased steadily to reach about 15,900 m/d (100,000 bopd) in 1962. At that time, the only ones being exploited were the Recรดncavo basin and the onshore portions of the Sergipe-Alagoas basin. In 1970, the first commercial offshore strike was achieved in the Sergipe-Alagoas basin. In the next two decades, onshore and offshore activities were developed in Potiguar, Espรrito Santo, Santos and Amazon basins. However, the most prolific activities were carried out in offshore Campos basin, in the Southeast of the country, whose exploitation made the Brazilian oil production really boost, to reach 79,500 m/d (500,000 bopd) in 1984. In 1985 the giant Marlim field was discovered in Campos basin, in water depths ranging from 600 to 1100 m (2,000 to 3,609 ft). Its production began in 1991 and water injection started in 1994. Production today exceeds 100,000 m/d (630,000 bopd). In 1996, the giant Roncador field was discovered, in water depths ranging from 1,500 to 2,000 m (4,921 to 6,562 ft). The first oil came only 27 months later, but an accident with the production platform P-36, in March of 2001, delayed the field development. The development of Marlim, Roncador an other fields in Campos basin, made the Brazilian oil production double in the last decade (see Figure 2). Nowadays, the total country daily output reaches about 238,000 m/d (1,500,000 bopd). The proved reserves are 1. 53ร10 m (9. 65ร10 barrels). As the producing areas in the Northeast became more mature, the Campos basin production took the lead (see Figure 3). Campos basin responds for 74% of the total output and 81% of the proved reserves. In spite of this fast production increase, the country is still an oil importer, since its production satisfies about 75% of its needs.
- South America > Brazil > Bahia (1.00)
- South America > Brazil > Sergipe > South Atlantic Ocean (0.85)
- South America > Brazil > Alagoas > South Atlantic Ocean (0.85)
- South America > Brazil > Rio de Janeiro > South Atlantic Ocean (0.68)
- Geophysics > Seismic Surveying > Seismic Processing (0.68)
- Geophysics > Time-Lapse Surveying > Time-Lapse Seismic Surveying (0.47)
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.89)
- South America > Brazil > Rio de Janeiro > South Atlantic Ocean > Campos Basin > Marlim Field > Macae Formation (0.99)
- South America > Brazil > Rio de Janeiro > South Atlantic Ocean > Campos Basin > Marlim Field > Lago Feia Formation (0.99)
- South America > Brazil > Rio de Janeiro > South Atlantic Ocean > Campos Basin > Garoupa Cluster > Namorado Field (0.99)
- (12 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reserves Evaluation (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Waterflooding (1.00)
- (3 more...)
Abstract Gas production from the UKCS commenced in 1967 and oil production in 1975. The North Sea area is now very much a mature province with the large fields in the Southern, Central and Northern North Sea producing at significantly below their early plateau production rates. Here the drive is to maximise the overall economic hydrocarbon recovery from the province, by making the best use of the infrastructure that has been built up to bring in new discoveries and improve recovery from the mature fields. New areas (deeper, harsher climate) are being opened up for exploration on the Atlantic Margin. This paper reviews the evolution of the mature areas of the UKCS, with case studies to illustrate the technical challenges that have been overcome. Over the years government and industry have expended considerable resources in developing innovative techniques for improved hydrocarbon recovery. These range from developments in the application of EOR processes to advances in drilling and reservoir management technology, including novel seismic techniques to identify new or bypassed oil. Technological advances have also unlocked reserves in heavy oils and in high-pressure high-temperature (HPHT) condensate fields, which were left undeveloped until the 1990s. Finally the potential for further exploitation and life extension of the UKCS as a significant hydrocarbon province will be reviewed. This will cover perceived technology gaps in opening up the new areas in deeper water, opportunities for redeveloping mature fields using new technology, combining IOR with carbon dioxide sequestration, and the need to drive down costs to be competitive in the international arena, while honouring environmental commitments. Introduction Oil exploration and production in the UK began onshore in the early part of the 20th century in the East Yorkshire, Lincolnshire and East Midlands areas. Later the interest extended to include the Dorset basin in the south of England. These were typically mechanical pump assisted fields producing a few 100 bbls/day/well. The first offshore gas field, West Sole in the Southern North Sea (SNS), was discovered in 1965 and brought onstream in 1967. Oil was first discovered in the Central North Sea (CNS) in 1969 and the first oilfield to come onstream was Argyll in 1975, followed soon after by the Forties field. The UK became self-sufficient in oil around 1980. Oil production on the UKCS has followed a typical exploitation path for a hydrocarbon producing area with large conventional fields being developed first and thereafter smaller fields utilising the infrastructure. It has now entered a third phase with the development of technically more difficult fields such as heavy oil fields and High Pressure High Temperature (HPHT) fields. A recent paper reviewed the UKCS heavy oil fields, so the primary focus of this paper is light oil fields. In the early days the industry used the term Enhanced Oil Recovery (EOR) to describe the deliberate injection of an alternative fluid to displace further oil from reservoir rock, over and above the standard pressure maintenance strategy (waterflooding for most UKCS oil fields). In the 1990s the industry began to use the term Improved Oil Recovery (IOR) to cover any operation (including the EOR techniques) that increased oil recovery above the figure that had been initially accepted as economically and technically exploitable. โImproved Hydrocarbon Recoveryโ is a more general term that will cover all hydrocarbon types, including gas, but it is not commonly used as an acronym.
- North America > United States > Texas (1.00)
- Europe > United Kingdom > North Sea > Northern North Sea (1.00)
- Europe > United Kingdom > England (1.00)
- (2 more...)
- Geophysics > Seismic Surveying > Surface Seismic Acquisition (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Time-Lapse Surveying > Time-Lapse Seismic Surveying (0.93)
- Geophysics > Seismic Surveying > Seismic Interpretation (0.67)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (92 more...)
Abstract It is generally believed that integration of different types of data can significantly improve reservoir description. To verify this hypothesis, a field case study was conducted on the complex Boonsville reservoir system located in the Fort Worth Basin in North-Central Texas. The zone of interest, Caddo, was interpreted in 3D seismic post-stack data using geophysical means, and different windowed seismic attributes (SA) were extracted around the time horizon. The structure and pay thickness maps were created using both seismic and well data. Correlation between well porosity and SA was established and utilized to generate porosity maps using several geostatistical techniques such as ordinary kriging, kriging with an external drift, and co-kriging. The technique of alternating conditional expectations (ACE) was implemented to maximize the correlation between well porosity and seismic attribute in a transformed space. Integration of this static reservoir model with dynamic (production) data was performed using the flow simulator, ECLIPSE. It was observed that large-scale features played much more important role in matching historical data than small-scale features. Incorporation of seismic data did not significantly improve the history match; however, it may be useful in drilling step-out wells. Introduction The objective of this study was to integrate 3-D seismic, well and production data to develop a reservoir model, and to analyze the value of integration of seismic data. The Boonsville field was chosen for this work because all these data types are publicly available from the Bureau of Economic Geology (BEG) at the University of Texas in Austin. These data were generated as part of the Secondary Gas Recovery (SGR) project supported by Gas Research Institute and the U.S. Department of Energy. The data set contains a time-migrated 3-D seismic volume, digitized well logs, depth to the genetic sequence boundaries interpreted from these logs, well completion, production and petrophysical data for 38 wells, a vertical seismic profile and the explosive-source checkshot data. Field Background The Boonsville field is located in Jack and Wise counties in the Fort Worth Basin in North-Central Texas (Fig. 1). The field is one of the largest natural gas fields in the U.S. It produces gas with some oil, which comes from conglomeratic sandstones deposited during the Atoka Stage of the Middle Pennsylvanian Period. Effective characterization and exploitation of the field is difficult because sandstone reservoirs are thin and discontinuous. The horizon Caddo was chosen for this study because it is the only oil producing horizon that allows separation of its input from the overall production when several other gas producing intervals are commingled. The depth to the top of Caddo horizon ranges from 4,500 to 4,750 ft sub-sea. The initial reservoir pressure is about 1550 psia. 3D Seismic Interpretation The rectangular region with 3-D seismic available for public study has an areal coverage of 5.5 mi (Fig. 2). This full-fold, time-migrated 3-D data volume is acceptable for structural interpretation; however, for the purposes of the extraction of seismic attributes it is critical to ensure that the amplitude spectra are preserved during the data processing. Unfortunately, the pre-stack data are not available in the public domain. Therefore, it was assumed that a minor distortion of the amplitude spectra took place during the processing of the field data.
- North America > United States > Texas > Wise County (0.45)
- North America > United States > Texas > Tarrant County > Fort Worth (0.45)
- Geology > Geological Subdiscipline (0.87)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.74)
- Geophysics > Seismic Surveying > Surface Seismic Acquisition (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying > Vertical Seismic Profile (VSP) (0.88)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (0.54)
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > North America Government > United States Government (0.54)
Abstract Sequential application of three-dimensional (3-D) seismic surveys has led to the concept of time-lapse (4-D) seismic monitoring. Changes in reservoir geophysical attributes during the life of a reservoir can be predicted by calculating the attributes as a function of time using a petrophysical algorithm in a flow simulator. The integrated flow model is described, and then applied to the following scenarios: solvent injection into a volatile oil reservoir; and carbon dioxide flooding of an undersaturated oil reservoir. Additional data needed by an integrated flow simulator for miscible gas injection processes are discussed. Introduction Time-lapse (4-D) seismic monitoring is the comparison of two seismic surveys conducted at two different points in time in the same area of interest. Differences in seismic response between the two surveys provide information about changes in reservoir properties that effect the transmission of seismic disturbances. These differences are especially useful, when they are significant, because it provides direct information about properties between wells. Time-lapse seismic monitoring, also called 4-D seismic, is becoming a cost-effective tool for improving reservoir characterization, locating bypassed reserves, and identifying the movement of fluid interfaces. Current research is seeking methodologies for maximizing the amount of information that can be obtained from 4-D seismic. This paper describes a methodology that is based on the calculation of reservoir geophysical attributes using an integrated flow model. The integrated flow model is the combination of a widely used petrophysical model with a 3-D, three-phase, multicomponent flow simulator to calculate the following reservoir geophysical attributes: acoustic impedance, reflection coefficient, compressional velocity; and shear velocity. These attributes are calculated at different times throughout the life of the reservoir. The calculation of reservoir geophysical attributes by an integrated flow model has several useful purposes. In addition to determining the feasibility of applying 4-D seismic monitoring to a particular reservoir environment, the integrated flow model is an efficient tool for generating information that will help the reservoir management team optimize a 4-D seismic monitoring program and prepare more accurate production forecasts. The integrated flow model provides information which can be used to predict which reservoir geophysical attribute, or combination of attributes, will yield the optimum response at different points in the life of the reservoir. The usefulness of information from an integrated flow model to reservoir management is discussed for the following scenarios: solvent injection into a volatile oil reservoir; and carbon dioxide flooding of an undersaturated oil reservoir. One of the difficulties associated with the incorporation of a petrophysical model into a flow simulator is the need for information that is not normally included. The additional data needed by an integrated flow simulator for miscible gas injection processes are discussed. Integrated Flow Model The integrated flow model consists of a petrophysical model and a traditional flow model. The flow simulator used in this study is a multicomponent, pseudomiscible simulator called IFLO. It includes a petrophysical model and is a Fortran 90 program that contains the best features of the extended black oil simulator MASTER and the black oil simulator with a petrophysical model BOAST4D. Fluid Properties Model. IFLO uses the extended fluid properties model coded in the simulator MASTER. It is an adaptation of Chase and Todd's mixing parameter method. Data consistency requires that the extended fluid property model simplifies to the conventional black oil fluid property model when no solvent is present. Fluid Properties Model. IFLO uses the extended fluid properties model coded in the simulator MASTER. It is an adaptation of Chase and Todd's mixing parameter method. Data consistency requires that the extended fluid property model simplifies to the conventional black oil fluid property model when no solvent is present.
- Energy > Oil & Gas > Upstream (1.00)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (0.95)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Seismic (four dimensional) monitoring (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (1.00)
Abstract The Grayburg reservoir of the Foster - South Cowden field has been produced since 1938 and water-flooded since 1962. Production had declined to near abandonment level at the start of this project. The initial approach to construction of the flow model was conventional. Logs and cores provided the basis for a geological model. Production data was assembled and validated as were the few measured pressures taken early in the fields history. Production testing of all wells was initiated with new equipment to provide accurate current production data. Pressure transient testing of all wells was initiated to provide accurate current pressures. History matching pressures and water cuts validated the flow model and the flow model has since guided field operation, subject to the limits imposed by the spacing of the well data which was one reliable well log per 32 acres of reservoir. At this scale compartmentalization, heterogenialy, of the reservoir was obvious, requiring the use of 3-D seismic to define porosity in the area between wells. The 3-D seismic data set was reprocessed to retain high frequencies, thereby improving vertical resolution to the range of 50 feet. Seismic traces sample areas are 110 by 110 feet (bin size). Seismic inversion model traces exhibit a high degree of correlation to the well log data and a correlation was developed between seismic velocities and porosity for each geologic zone. The correlation was used to develop porosity maps for each zone used in the flow model. The resulting flow model was validated through the history matching process and used to guide the redevelopment of the waterflood. P. 115
- Geology > Sedimentary Geology (0.95)
- Geology > Geological Subdiscipline > Stratigraphy (0.69)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.49)
- Geophysics > Seismic Surveying > Surface Seismic Acquisition (0.89)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.69)
- Geophysics > Seismic Surveying > Seismic Processing (0.68)
- Geophysics > Seismic Surveying > Seismic Interpretation (0.65)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (32 more...)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (1.00)
Abstract Case studies of the applications of three new technologies โ 3-D Seismic, Horizontal Drilling and Multi-lateral Drilling - in Saudi Arabian Oil fields are described. Results demonstrate that:3-D Seismic surveys arc resulting in better field delineation and more accurate reservoir characterization. Better volumetric estimates result in setting realistic field development and production targets. Better reservoir characterization facies distributions and saturation domains-aids in cost-effective field development plans and efficient reservoir management programs. Horizontal and high- angle wells are aiding in producing:relatively low permeability tight reservoirs where vertical wells cannot produce at economic rates, oil pockets trapped within the random tar-filled areas of the reservoir as well as in the attic area, water-free oil from water-underlain, seemingly stagnant thin oil columns, and oil zones underlying large gas caps without excessive gas production. Multi-lateral wells are providing means to produce:low permeability tight reservoirs with each lateral draining a separate area, and high flow-capacity contrast multi-layered reservoirs promoting simultaneous production from various layers. Implementation of these technologies has had a pronounced effect on reducing capital and operating costs. Development planning has become more cost-effective, oil production rate declines are being arrested' plateau oil rates are being sustained over longer duration, and oil recoveries are being improved. P. 415
- Asia > Middle East > Saudi Arabia > Eastern Province > Al-Ahsa Governorate > Arabian Basin > Widyan Basin > Ghawar Field > Lower Fadhili Formation (0.99)
- Asia > Middle East > Saudi Arabia > Eastern Province > Al-Ahsa Governorate > Arabian Basin > Widyan Basin > Ghawar Field > Khuff D Formation (0.99)
- Asia > Middle East > Saudi Arabia > Eastern Province > Al-Ahsa Governorate > Arabian Basin > Widyan Basin > Ghawar Field > Khuff C Formation (0.99)
- (4 more...)
- Well Drilling > Drilling Operations > Directional drilling (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery (1.00)
Abstract This paper presents a systematic procedure for integrating 3-D seismic data and production data to develop a detailed reservoir description. The procedure involves three steps. Step one includes the inversion of the seismic data to construct impedance distribution with the well core and log data. Step two includes the simulation of porosity values consistent with the impedance data. The last step involves the estimation of permeability values consistent with its relationship with porosity as well as the dynamic information. All these three steps are discussed in detail. The procedure is further validated by applying it to both synthetic and the field data. The results obtained have been satisfactory illustrating the suitability of the method. Introduction 3D seismic data are often collected for field exploration. Compared to the well data, seismic data have better areal coverage, but poor vertical resolution. The seismic data mainly reflect the acoustic property of formation, such as the acoustic impedance which describes a static property of the reservoir. By integrating the seismic and well data, a better impedance distribution within the reservoir can be obtained. This requires a better inversion technique which can integrate these two types of information. A better integration of seismic and well data may reduce the non-uniqueness of the solution and enhance the resolution to describe the heterogeneities in the reservoir. Although improved, the integrated static description of the reservoir may not be sufficient to reproduce the dynamic behavior. For a better reservoir management, it is critical to predict the future performance of the reservoir. Before predicting the future performance, we first need to reproduce the prior performance. As a result, we need a reservoir description which is not only consistent with the static properties. but is also capable of reproducing the past performance. Background In 1977, Lavergne and William presented a work regarding the conversion of seismic amplitude data into pseudo-velocity by deconvolution. In 1979, Lindseth also presented a similar method to obtain the pseudo-log. In this type of inversion, the wavelet was determined in the well location. The other seismic traces are inverted using this wavelet by applying the deconvolution method. Back to 1970, Backus and Gilbert proposed the generalized linear inversion (GLI) theory. Since then, many investigators applied this theory for seismic data processing and inversion. Cooke applied the technique to invert the CDP (Common Depth Point) seismic trace into impedance. GLI remains a popular inversion technique. The conventional methods discussed above have some disadvantages:the solution is non-unique; they require knowledge of the boundary impedance, i.e., the starting impedance. In 1991, Sen and Stoffa applied simulated annealing and genetic algorithm for the layer velocity determination for seismic data processing. These methods have the capability to integrate other information and may Overcome some of the difficulties encountered in conventional methods. Recently, Huang and Kelkar implemented these algorithms for 3D seismic inversion by integrating the well information and seismic data into the inversion process. In addition, a new efficient algorithm called modified stochastic hill climbing was also used for the inversion process. P. 157
- Geophysics > Seismic Surveying > Surface Seismic Acquisition (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.49)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Stratton Field > Frio Formation (0.99)
- North America > United States > Texas > Frio Formation (0.99)
- North America > United States > Kansas > Whitmer Lease > Phillips Field (0.99)