A Jurrasic oilfield in Saudi Arabia is characterized by black oil in the crest and with mobile heavy oil underneath and all underlain by a tar mat at the oil-water contact. The viscosities in the black oil section of the column are fairly similar and are quite manageable from a production standpoint. In contrast, the mobile heavy oil section of the column contains a large continuous increase in asphaltene content with increasing depth extending to the tar mat. The tar shows very high asphaltene content but not monotonically increasing with depth. Because viscosity depends exponentially on asphaltene content in these oils, the observed viscosity varies from several to ~ 1000 centipoise in the mobile heavy oil and increases to far greater viscosities in the tar mat. Both the excessive viscosity of the heavy oil and the existence of the tar mat represent major, distinct challenges in oil production. Conventional PVT modeling of this oil column grossly fails to account for these observations. Indeed, the very large height in this oil column represents a stringent challenge for any corresponding fluid model. A simple new formalism to characterize the asphaltene nanoscience in crude oils, the Yen-Mullins model, has enabled the industry's first predictive equation of state (EoS) for asphaltene gradients, the Flory-Huggins-Zuo (FHZ) EoS. For low GOR oils such as those in this field, the FHZ EoS reduces to the simple gravity term. Robust application of the FHZ EoS employing the Yen-Mullins model accounts for the major property variations in the oil column and by extension the tar mat as well. Moreover, as these crude oils are largely equilibrated throughout the field, reservoir connectivity is indicated in this field. This novel asphaltene science is dramatically improving understanding of important constraints on oil production in oil reservoirs.
Dong, Chengli (Shell International E&P (Rijswijk)) | Petro, David Robert (Marathon Oil) | Hayden, Ron S. (Schlumberger) | Zuo, Julian Youxiang (Schlumberger) | Pomerantz, Andrew (Schlumberger) | Mullins, Oliver C. (Schlumberger)
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.
In recent years, there has been a growing recognition that complex reservoir architectures and complex fluid distributions are often the norm. Reservoir fluids undergo many dynamic processes over time, which may lead to highly complex oil columns. Factors that give rise to fluid complexities include current/multiple reservoir charging, biodegradation, water/gas washes, and leaky seals. Reservoir architecture is often complex with multiple compartments, baffles and tortuous connectivity. Because the reservoir fluids often exhibit large variations, reservoir compartmentalization can appear as stair-step discontinuous fluid properties. In contrast, well connected reservoirs exhibit smooth distributions of reservoir fluid properties. Analysis of reservoir fluid property distribution often functions as a proxy for reservoir connectivity in field scale, which has been supported by many field case studies.
Mishra, Vinay Kumar (Schlumberger) | Skinner, Carla (Husky Energy Inc.) | MacDonald, Dennis (Husky Energy Inc.) | Hammou, Nasreddine (Saudi Aramco Shell Ref Co) | Lehne, Eric (Schlumberger) | Wu, Jiehui (Schlumberger) | Zuo, Julian Youxiang (Schlumberger) | Dong, Chengli (Shell International E&P (Rijswijk)) | Mullins, Oliver C. (Schlumberger)
It has long been recognized that condensates can exhibit large compositional gradients. It is increasingly recognized that black oil columns can also exhibit substantial gradients. Moreover, significant advances in asphaltene science have provided the framework for modeling these gradients. For effective field development planning, it is important to understand possible variations in the oil column. These developments in petroleum science are being coupled with the new technology of downhole fluid analysis (DFA) to mitigate risk in oil production.
In this case study, DFA measurements revealed a large (10×) gradient of asphaltenes in a 100-m black oil column, with a corresponding large viscosity gradient. This asphaltene gradient was traced to the colloidal description of the asphaltenes, which yielded two conclusions: the asphaltenes are vertically equilibrated, consequently vertical connectivity is indicated, and the asphaltenes are partially destabilized. Vertical interference testing (VIT) was performed at several depths and confirmed the vertical connectivity of the oil column, with four of the five tests showing unambiguous vertical connectivity consistent with the overall connectivity implied by DFA. Geochemical analysis indicates that the instability was due to some late gas and condensate entry into the reservoir. For mitigation of production risk, flow assurance studies were performed and showed that while the asphaltenes are indeed partially destabilized, there is no significant associated problem. Moreover, thin sections of core were analyzed to detect possible bitumen. A very small quantity of bitumen was found, again confirming the asphaltene analysis; however, geochemical studies and flow assurance studies confirmed that this small amount of bitumen is not expected to create any reservoir issues.
Using new science and new technology to identify and minimize risk in oil production in combination with pressure transients addressed reservoir connectivity and provided a robust, positive assessment.
A significant portion of the world's hydrocarbon reserves is found in heavy oil reservoirs. Heavy oils are often found in shallow and highly unconsolidated reservoirs, or sometimes in deep, tight formations. Often the high asphaltic content of these oils results in relatively higher oil density and viscosity; hence, their lower reservoir mobility poses significant challenges to both sampling and PVT data measurements. Furthermore, modeling these fluids for reservoir evaluation requires special techniques to capture their unique phase behavior.
The challenges of representative down-hole or surface fluid sample acquisition demand customized sampling methods to deal with:
• low oil mobility
• sand production from unconsolidated formations
• high asphaltene content and resulting high gradients
• formation of water-in-oil emulsion during co-production of water or gas lift operations or addition of diluents
In addition, the prerequisite for laboratory measurement is special sample preparation to remove emulsified water. These high viscosity oils exhibit slower gas liberation below the bubble point and hence delayed gas-phase formation, thus making "true?? oil property measurements a challenge. Difficulties associated with fluid modeling include characterizing apparent bubble point behavior, large viscosity changes with pressure and temperature, and asphaltene dropout.
In this paper, we present a comprehensive methodology for heavy oil sampling and characterization in unconsolidated sands as well as in low permeability reservoirs. We present field examples to highlight the challenges and illustrate the methodology for fluid sampling, down-hole fluid analysis, laboratory PVT data acquisition, and modeling. Sampling methods for heavy and asphaltic oils were custom designed with special tools and sensors to obtain representative samples and precise down-hole fluid analysis data. New laboratory techniques were developed to prepare the samples for analysis and to distinguish between the "true?? and "apparent?? bubble point behavior exhibited by the heavy oil due to its non-equilibrium behavior. Fluid models based on a special equations of state (EoS) were employed for accurate description of heavy oil fluid phase behavior. In particular, we successfully applied the industry's first EoS for asphaltene gradients in heavy oil reservoirs that match down-hole fluid data.
Mullins, Oliver C. (Schlumberger) | Zuo, Julian Y. (Schlumberger) | Andrew, A. Ballard (Schlumberger) | Pfeiffer, Thomas (Schlumberger) | Andrew, E. Pomerantz (Schlumberger) | Dong, Chengli (Shell Exploration and Production Inc) | Elshahawi, Hani (Shell Exploration and Production Inc) | Cribbs, Myrt E. (Chevron North America)
Dong, Chengli (Shell) | Petro, David (Marathon) | Latifzai, Ahmad S. (Shell) | Zuo, Julian Y. (Schlumberger) | Pomerantz, Andrew E. (Schlumberger) | Mullins, Oliver C. (Schlumberger) | Hayden, Ron S. (Schlumberger)