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Collaborating Authors
Fluid modeling, equations of state
Evaluation of Reservoir Connectivity from Downhole Fluid Analysis, Asphaltene Equation of State Model and Advanced Laboratory Fluid Analyses
Dong, Chengli (Shell) | Petro, David (Marathon) | Latifzai, Ahmad S. (Shell) | Zuo, Julian (Schlumberger) | Pomerantz, Andrew E. (Schlumberger) | Mullins, Oliver C. (Schlumberger)
Abstract Characterization of complicated reservoir architecture with multiple compartments, baffles and tortuous connectivity is critical; additionally, reservoir fluids undergo dynamic processes (multiple charging, biodegradation and water/gas washes) that lead to complex fluid columns with significant property variation. Accurate understanding of both reservoir and fluids is critical for reserve assessment, field management and production planning. In this paper, a methodology is presented for reservoir connectivity analysis, which integrates reservoir fluid property distributions with an asphaltene Equation of State (EoS) model developed recently. The implications of reservoir fluid equilibrium are treated within laboratory experimentation and equation of state modeling. In addition to cubic EoS modeling for light end gradients, the industry's first asphaltene EoS the Flory-Huggins-Zuo EoS is successfully utilized for asphaltene gradients. This new EoS has been enabled by the resolution of asphaltene nanoscience embodied in the Yen-Mullins model. Specific reservoir fluid gradients, such as gas-oil ratio (GOR), composition and asphaltene content, can be measured in real time and under downhole conditions with downhole fluid analysis (DFA) conveyed by formation tester tools. Integration of the DFA methods with the asphaltene EoS model provides an effective method to analyze connectivity at the field scale, for both volatile oil/condensate gas reservoirs with large GOR variation, and black oil/mobile heavy oil fields with asphaltene variation in dominant. A field case study is presented that involves multiple stacked sands in five wells in a complicated offshore field. Formation pressure analysis is inconclusive in determining formation connectivity due to measurement uncertainties; furthermore, conventional PVT laboratory analysis does not indicate significant fluid property variation. In this highly under-saturated black oil field, measurement of asphaltene content using DFA shows significant variation and is critical for understanding the reservoir fluid distribution. When integrated with the asphaltene EoS model, connectivity across multiple sands and wells is determined with high confidence, and the results are confirmed by actual production data. Advanced laboratory fluid analysis, such as two-dimensional gas chromatography, is also conducted on fluid samples, which further confirms the result of the DFA and asphaltene EoS model.
- North America > United States (0.68)
- North America > Canada > Alberta (0.24)
- Geology > Petroleum Play Type > Unconventional Play > Heavy Oil Play (0.54)
- Geology > Geological Subdiscipline > Geochemistry (0.48)
Integrated Asset Modeling for Reservoir Management of a Miscible WAG Development on Alaska's Western North Slope
Roadifer, R. D. (ConocoPhillips Alaska Inc) | Sauvé, R.. (Schlumberger) | Torrens, R.. (Schlumberger Middle East SA) | Mead, H. W. (ConocoPhillips Alaska Inc) | Pysz, N. P. (ConocoPhillips Alaska Inc) | Uldrich, D. O. (ConocoPhillips Alaska Inc) | Eiben, T.. (ConocoPhillips Canada)
Abstract An integrated asset modeling (IAM) approach has been implemented for the Alpine Field and eight associated satellite fields on the Western Alaskan North Slope (WNS) to maximize asset value and recovery. The IAM approach enables the investigation of reservoir and facilities management options under existing and future operating constraints. Oil, gas and water production from these fields are processed at the Alpine Central Facility (ACF). A number of local constraints exist for the asset, such as the requirement that all associated gas be used for facilities power generation, gas lift or re-injection. All produced water must be re-injected and, for pipeline integrity reasons, must be segregated from imported make-up sea water used for injection. Additionally, surface gas and water handling capacity is limited at the ACF. To further complicate matters, gas injected for EOR purposes is enriched such that it is miscible or near-miscible at reservoir conditions. These conditions create a unique and changing relationship between the oil, gas and water production, gas lift, miscible water alternating gas (MWAG) injection, lean gas injection, facilities constraints and injection availability. The scope of the current IAM project has been multi-fold. Optimization of oil production across all WNS fields requires the placement of injection fluids be simultaneously optimized. The optimization procedure begins by allocation of oil production targets based on current operating conditions, the potentials of the wells in each field to deliver fluids, and total gas lift availability. Excess gas compression capacity is utilized for gas lift and is allocated via an incremental gas-oil ratio sort on the production wells. Given the constraints on water injection noted above, optimization of injection fluids begins by determining pump requirements for produced water and the optimal field or injection manifold placement of the produced water. Following this, optimized placement of the miscible injectant (MI) and lean gas injectant (LGI) is determined based on a dynamic MWAG scheduling methodology developed to maximize oil recovery and ensure the number of gas injection wells have sufficient capacity to inject the required volume of gas in each reservoir. The volumetric split of gas into MI and LGI streams falls out directly from the specification of a target minimum miscibility pressure (MMP) constraint for the MI and the volume of condensates driven off the top of the condensate stabilizer column at the process facility. Finally, the volume of the make-up fluid (sea water) is determined based on the minimum of the remaining pump capacity or potential of the remaining wells to inject the water and allocated to each field based on a fractional oil voidage replacement scheme. Maximizing production across multiple fields necessarily requires that the best player (well) plays, regardless of the field to which it belongs. This requirement relates to both instantaneous production as would be considered under a gas lift optimization scenario as well as the longer term MWAG performance and recovery of each individual well pattern across all the fields. The IAM technology utilized for managing the WNS fields consists of full-field compositional reservoir simulation models for each reservoir integrated with a pipeline surface network model and a process facility model. Spreadsheet based allocation routines and advanced mathematical coupling algorithms complete the IAM model enabling not only the prediction of the assets’ performance under the aforementioned constraints, capacities and operating conditions, but to optimize overall performance and analyze the impact of decisions. To the authors’ knowledge, this is the first time integrated asset modeling has been applied to bring the entire production stream including reservoir, wellbore, surface network and process simulation together for planning and managing MWAG injection to optimize recovery from an existing development.
- North America > United States > Texas (0.92)
- North America > United States > Alaska > North Slope Borough (0.88)
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.36)
- North America > United States > Alaska > North Slope Basin > Kuparuk River Field (0.99)
- Asia > Middle East > Israel > Tel Aviv District > Southern Levant Basin > National Field (0.97)
- North America > United States > Alaska > North Slope Basin > Western North Slope > Colville River Field > Alpine Field > Kingak Formation (0.94)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Miscible methods (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Gas-injection methods (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (1.00)
- (2 more...)
Wanted Dead or Live: Crude Cocktail Viscosity: A Pseudo-Component Method to Predict the Viscosity of Dead Oils, Live Oils, and Mixtures
Yarranton, Harvey W. (Department of Chemical and Petroleum Engineering University of Calgary) | van Dorp, Johan J. (Shell Canada Ltd.) | Verlaan, Marco L. (Shell Canada Ltd.) | Lastovka, Vaclav (Shell Chemicals Americas Inc.)
Abstract A broadly applicable methodology is presented to reliably predict crude oil liquid viscosity from only a gas chromatographic assay composition (C30+ is recommended). The viscosity model employs a Walther-type correlation of double log viscosity with log temperature to predict the viscosity of dead and live crude oils and mixtures. The model has three parameters: the slope and intercept of the Walther plot and a viscosibility factor to account for pressure effects. Simple mass based mixing rules are applied on these three parameters to obtain mixture viscosity. The three parameters were correlated to component molecular weight and therefore a gas chromatographic assay is the only required input apart from the temperature and pressure. The methodology was developed from a Western Canadian dataset of two bitumens, one heavy oil, and one condensate, and then tested on an independent dataset of 10 conventional and heavy crude oils from the Gulf of Mexico, the Middle East, Asia, and Europe. The model provides un-tuned viscosity predictions within a factor of two of the measured values for dead and live crude oils ranging in viscosity from 0.5 to 500,000 mPa.s. A single multiplier is used to tune the model. Models tuned to dead oil data predict live oil viscosities and mixtures of oils with solvents to within 30% of the measured values. Models tuned to the viscosity at the saturation pressure predict the effect of temperature and pressure to within 20% of the measured values. The method retains its accuracy when components are lumped into a few pseudo-components and is ideally suited for use in simulators for accurate liquid phase viscosity predictions over a wide range of compositions, pressures, and temperatures. It would be necessary to include the proposed mixing rules in numerical simulators. An additional advantage of the method is the reduction in viscosity measurements needed to construct an accurate viscosity model.
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- North America > Cuba > Gulf of Mexico (0.89)
- Europe (0.89)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Oil sand, oil shale, bitumen (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (1.00)
- Facilities Design, Construction and Operation > Unconventional Production Facilities > Oil sand/shale/bitumen (1.00)
Abstract Integrated modeling is becoming a necessary tool in the petroleum industry to manage the value chain of different models. Reservoir models commonly utilize a simple fluid model to reduce computational time. However, the downstream models often require a more detailed EOS fluid model to perform surface-process facility modeling. This paper presents a dynamic delumping method to generate detailed compositional streams from either black-oil or compositional (lumped-EOS) reservoir simulations, performed as a simple post-processing step. A set of phase-specific, pressure-dependent split factors are used to perform dynamic delumping. The split factors are generated from simulated depletion PVT experiments using a detailed-EOS model. Delumping is performed phase-wise at the well-connection level, for each time step of the reservoir simulator. For gas injection processes, the amount of injection gas is estimated from stream information and, accordingly, removed from the stream before applying the phase-specific pressure- dependent split factors. Different split factor sets are used when the reservoir model has multiple PVT regions. We have run many reservoir simulation cases using different production mechanisms and reservoir fluids. Compared with detailed-EOS simulations, the proposed method gives near-exact results for depletion, and excellent agreement in gas injection cases. Dynamic delumping also works with complex fluid systems exhibiting large in-situ compositional (GOR) variations. For injection gas cases, improved accuracy is obtained using a tracer option in the reservoir simulator, to better estimate injection-gas quantity. This approach requires negligible cpu compared with detailed-EOS reservoir simulation. Dynamic delumping is applied as an automated post-processing for any reservoir simulator. The results of our work provide a key technology for integrating subsurface and surface petroleum models, ensuring greater consistency in the complete value chain and enabling engineers to optimize assets, both locally and globally.
- Europe (1.00)
- North America > United States > Texas (0.46)
- Europe > Norway > Barents Sea > Hammerfest Basin > PL 229 > Block 7122/8 > Goliat Field > Kapp Toscana Group > Realgrunnen Subgroup > Kapp Toscana Group > Realgrunnen Subgroup > Snadd Formation > Realgrunnen Subgroup > Tubåen Formation > Sassendalen Group > Kobbe Formation (0.99)
- Europe > Norway > Barents Sea > Hammerfest Basin > PL 229 > Block 7122/8 > Goliat Field > Kapp Toscana Group > Realgrunnen Subgroup > Kapp Toscana Group > Realgrunnen Subgroup > Snadd Formation > Realgrunnen Subgroup > Tubåen Formation > Klappmyss Formation > Kobbe Formation (0.99)
- Europe > Norway > Barents Sea > Hammerfest Basin > PL 229 > Block 7122/8 > Goliat Field > Kapp Toscana Group > Realgrunnen Subgroup > Kapp Toscana Group > Realgrunnen Subgroup > Snadd Formation > Realgrunnen Subgroup > Sassendalen Group > Sassendalen Group > Kobbe Formation (0.99)
- (61 more...)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Gas-injection methods (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (1.00)
Abstract Gas condensate reserves are typically underestimated when single phase compressibility factors are applied to two-phase systems. The effect is worse in rich gas condensate system with adverse implications for gas initially in place (GIIP) estimation, gas reserves, petroleum accounting, and pipeline designs. Accurate prediction of GIIP early in the life of a reservoir is therefore crucial in reaching development decisions and contractual agreements. Underestimation of reserves often results from approximations based on single phase (Standing and Katz) compressibility factors that do not consider two-phase regions that retrograde condensate reservoirs exhibit at pressures below the dew point. During isothermal pressure reduction below the dew point phase changes occur leading to liquid and gaseous phase separation in the reservoir. Consequently, there is need to introduce a two phase Z factor in material balance calculations to account for the phase changes in the reservoir. Ideally, Z-factors for gas reservoirs are determined from constant volume depletion (CVD) tests that simulate compositional changes during reservoir depletion. Sometimes the CVD test reports are not readily available or plans are non-existent for PVT data acquisition campaign. A methodology that employs a suitable correlation developed using fluid molar composition or gas gravity of reservoir fluid to obtain two-phase Z-factors (Z2p) is described. This compares quite well with actual data for retrograde condensate systems. This paper describes a correlational research design with two-phase Z-factor derived from the real gas law equation and single phase Z factor from Rayes et al (1992) to determine GIIP for gas condensate reservoirs. Z-factors were also determined from Dranchuk Abu-Kassem (1975) correlations using pseudo-reduced properties presented by Sutton (1985) and Kay (1936) mixing rules. The paper compares Z2p, Z-factors, GIIP values obtained from the CVD test and correlations and establishes that the two-phase Z-factor should be used for more precise estimation of GIIP in gas condensate reservoirs.
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Gas-condensate reservoirs (1.00)
- Reservoir Description and Dynamics > Reserves Evaluation (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (1.00)
Abstract This paper presents a novel approach for characterizing erosion wear in multiphase flow systems; it is based on Eulerian- Granular theorem rather than the conventional Eulerian-Lagrangian methodology. The first step in the novel approach is to characterize multiphase distribution based on the multi-fluid concept, which treats both the carrying fluid and entrained particles as continuous phases. The second step is to quantify the impinging velocity of particles using the granular model, which introduces particle-particle interaction and turbulence modification to carrying fluids beyond the fluid-particle and particle-fluid coupling considered in the Eulerian-Lagrangian approach. The final step is to determine the erosion rate by applying more representative particle velocity into a selected erosion correlation. In comparison to the Eulerian-Lagrangian approach, the Eulerian-Granular approach is able provide more realistic prediction on erosion profile in full particle loading range due to the least assumptions.
Abstract Capillary pressure (Pc) is one of the main factors governing the hydrocarbon distribution within a reservoir. Its determination usually requires expensive, time-consuming laboratory experiments on a restricted number of core samples, while the continuous Pc profile of a well is practically derived from the nuclear-magnetic-resonance (NMR) downhole logging measurements. This paper presents a robust and inexpensive method of predicting the continuous Pc profile of a well from rock models reconstructed using various well log data. The approach first generates a representative rock model for the formation at each given depth of interest. The rock model is constrained by formation parameters derived from the logging data and accounts for diagenetic processes such as compaction and precipitation of carbonate and clay minerals. Simulations of fluid flow and primary drainage are then performed on rock models to determine the Pc curve and absolute permeability. To test and validate our modeling approach, we select 16 sandstone core samples from various geologic settings to perform laboratory measurements and numermical simulations. Rock models are reconstructed using the measured grain-size distributions and grain mineralogy from core samples. The drainage Pc curves derived from rock models match well with laboratory measurements on the corresponding core samples, while Pc curves converted from NMR T2 distributions using the simple relationship of PcT2= ξ show differences in shape. Furthermore, the computed permeability of rock models show good agreement with the core permeability, mostly falling within the ± 2 times measurements. We have also applied the rock modeling technique to predict the continuous Pc and permeability profiles of a well. Formation grain-size distribution and mineralogy at each depth are derived from downhole measurements and used to generate rock models. Generally, our computed permeability falls within the same order of magnitude as the measurements on core samples from the same depth. The simulated Pc curves differ in shape from those converted from NMR T2 distributions. However, in this case it is unknown which one represents the real Pc curve due to the absence of laboratory core measurements.
- North America > United States > California (0.68)
- Europe (0.68)
- Asia (0.67)
- North America > United States > Texas > Harris County > Houston (0.28)
- Geology > Sedimentary Geology (1.00)
- Geology > Geological Subdiscipline (1.00)
- Geology > Mineral > Silicate > Phyllosilicate (0.68)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.50)
Abstract Capillary pressure can have a significant effect on multiphase flow in heterogeneous and fractured media, even when there is species transfer between the phases. Modeling the combined non-linearities from phase behavior and capillarity in the multiphase flow equations for heterogeneous and fractured media may be one of the most complicated problems in reservoir simulation. In this work, we present an efficient numerical scheme that uses higher-order methods for the first time to model capillarity in fully compositional three-phase flow. We introduce a simple local computation of the capillarity pressure gradients in the fractional flow formulation in terms of the total flux. Complications arising from gravity and capillarity are resolved in the upwinding with respect to phase fluxes. Our choice of the Mixed Hybrid Finite Element Method for the pressure and flux fields is an accurate and natural approach to compute the capillary pressure gradients and fluxes at the interface between regions of different permeabilities. We present various examples on both core- and large-scales to demonstrate powerful features of our capillary pressure modeling and the upwinding with gravity and capillary pressure. The examples include layered and fractured domains.
Copyright 2012, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in San Antonio, Texas, USA, 8-10 October 2012. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright. Abstract While several three-dimensional (3D) fracturing models exist for incompressible water-based fluids, none are able to capture the thermal and compositional effects that are important when using energized fluids. This paper introduces a new 3D, compositional, non-isothermal, fracturing model designed for compressible fracturing fluids. The new model predicts changes in temperature and fluid density. These changes are treated on a firm theoretical basis by using an energy balance equation and an equation of state, both in the fracture and in the wellbore. The model is capable of handling any multicomponent mixture of fluids and chemicals. Changes in phase behavior with temperature, pressure, and composition can be modeled. A new simulator has been developed based on the compositional model presented in this paper. The simulator is validated for traditional fluid formulations against known analytical solutions and against a well-established commercial fracturing simulator.
- Well Completion > Hydraulic Fracturing > Fracturing materials (fluids, proppant) (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (1.00)
Abstract Condensate to Gas Ratio (CGR) is a measure of liquid content of a naturally occurring hydrocarbon mixture that is classified as gas condensate at reservoir conditions. It is also one of the most important parameters that impacts the economics of gas projects. In many cases, especially in the case of stranded gas, significant project value will be drawn from the liquid content of the gas. There are number of difficulties in measuring the CGR of a gas stream in the laboratory, or sometimes simply the value may not be known. Some of the difficulties in lab processes (but not limited to) originate from: Non-equilibrium flash and carry-over Amount of the fluid used (volume constraints and errors) In this paper, we have developed a simple easy to use semi-empirical correlation that accurately estimates the CGR of a gas condensate system using fluid densities (or molecular weights). Fluid densities can be obtained either from gradient measurements or directly from the laboratory measurements. To our knowledge, there is no equivalent correlation published in literature. The proposed correlation can be used for: Exploration support to estimate the CGR ranges (valid up to CGR = 350 STB/MMSCF). Form a yard-stick to quality-check laboratory experiments. Generate necessary input parameters for other key gas properties: such as Z-factor, gas formation volume factor, Molecular weight, gas viscosity and even compositions. Reconciliation of contaminated samples against the measured or estimated fluid densities.
- Reservoir Description and Dynamics > Formation Evaluation & Management (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring (1.00)