Understanding Reservoir Fluid Dynamic Processes by Using Diffusive Models

The Yen-Mullins model of asphaltenes has enabled the development of the industry's first asphaltene equation of state (EOS) for predicting asphaltene concentration gradients in oil reservoirs, the Flory-Huggins-Zuo (FHZ) EOS. The FHZ EOS is built on the existing the Flory-Huggins regular solution model, which has been widely used in modeling the phase behavior of asphaltene precipitation in the oil and gas industry. For crude oil in reservoirs with a low gas/oil ratio (GOR), the FHZ EOS reduces predominantly to a simple form—the gravity term only—and for mobile heavy oil, the gravity term is simply based on asphaltene clusters. The FHZ EOS has been applied to different crude oil columns from volatile oil to black oil to mobile heavy oil all over the world to address key reservoir issues such as reservoir connectivity/compartmentalization, tar mat formation, nonequilibrium with a late gas charge, and asphaltene destabilization by integrating downhole fluid analysis (DFA) measurements and the Yen-Mullins model of asphaltenes. Asphaltene or heavy-end concentration gradients in crude oils are treated using the FHZ EOS explicitly incorporating the size of resin molecules, asphaltene molecules, asphaltene nanoaggregates, or/and asphaltene clusters. Field case studies proved the value and simplicity of this asphaltene or heavy-end treatment. Heuristics can be developed from results corresponding to the estimation of asphaltene gradients. Perylene-like resins with the size of ∼1 nm are dispersed as molecules in high-GOR light oils (condensates) with high fluorescence intensity and without asphaltenes (0 wt% asphaltene). Heavy asphaltene-like resins with the size of ∼1.5 nm are molecularly dissolved in volatile oil at very low asphaltene content. Asphaltene nanoaggregates with the size of ∼2 nm are dispersed in stable crude oil at a bit higher asphaltene content. Asphaltene clusters are found in mobile heavy oil with the size of ∼5 nm at even higher asphaltene content (typically >8 wt% based on stock-tank oil). All these studies are in accord with the observations in the Yen-Mullins model within the FHZ EOS analysis. Furthermore, the cubic EOS and FHZ EOS have been extended to a near critical fluid column with GOR changing from 2600 to 5600 scf/STB and API gravity changes from 34 to 41 °API. Data from the real-time third-generation of DFA were used to establish the early time EOS for advanced formation evaluation. The early-time EOS was updated after the laboratory PVT data were available. The results from the early-time EOS based on the new-generation DFA data were in accord with those from the updated one based on the pressure/volume/temperature (PVT) data. The large GOR gradient is well modeled by the cubic EOS assuming a small late gas charge from the crest to the base. The FHZ EOS with 1-nm diameter was employed to predict the fluorescence intensity gradient. This agrees that perylene-like resins with the size of ∼1 nm are dispersed as molecules in high-GOR light oil (rich gas condensate) with high fluorescence intensity and without asphaltenes (0 wt% asphaltene).

Understanding reservoir complexities such as compartmentalisation and compositional gradients early in the life of a field is crucial for its optimal development. That's especially true for deepwater reservoirs. Deepwater exploration and development has become a key area for most E&P companies because of the increased global demand for oil and gas and the limited accessibility of other potential unexploited resources. Modern wireline formation testers can provide a wide range of downhole rock and fluid properties at in-situ reservoir conditions and can help diagnose deepwater reservoir issues such as compartmentalisation and compositional grading early on. Advanced downhole fluid analysis (DFA) performed by wireline formation testing tools provides real time quantitative measurement of variation in asphaltene content as manifested though fluorescence and fluid coloration. Gas-oil ratio (GOR), another key determinant in asphaltene stability and gradients, is also measured downhole with the DFA technique. Building on recent advances in asphaltene molecular and colloidal physics, a new asphaltene equation of state (EoS) can be used to model asphaltene gradients in crude oils in the same way that a traditional cubic EoS is used for modeling GOR gradients. In this paper, we analyze a deepwater case study showing increases in asphaltenes content with depth due to destabilisation resulting from secondary gas charging at the top of the column and with solid hydrocarbon/tar depositing at the base. These seemingly contradictory observations are shown to follow from a new nanoscience model of asphaltenes. A simple model is proposed for this tar mat formation and consisting of disequilibrium gas charging, large disequilibrium gradients of asphaltene and GOR, asphaltene instability in the oil, and tar in the core. Likely compartmentalisation will also be identified using DFA analysis and verified using lab data analysis, allowing integration into the reservoir modeling process.

The modeling of hydrocarbon fluids in oil-field reservoirs is essential for optimizing production. In particular, the often large compositional variations of reservoir crude oils must be understood and modeled. The two most important chemical constituents that govern many chemical and physical properties of subsurface reservoir crude oils are the dissolved gas content, described by the gas−oil ratio (GOR), and the asphaltene content. The modeling of GOR variations of crude oils in reservoirs has been practiced routinely for many decades. However, proper modeling of the asphaltenes and/or heavy ends of reservoir crude oils has been precluded because of the lack of understanding of the chemical and physical properties of asphaltenes in crude oils. Recently, the modified Yen model has codified advances in asphaltene science by providing a framework for understanding the molecular and colloidal structure of asphaltenes in crude oils. Here, a thermodynamic model of asphaltenes in reservoir crude oils is developed that can incorporate the modified Yen model and thus can be used to treat reservoir crude oils. Our objective is to analyze the distribution of reservoir fluids, in particular the asphaltenes. This deviates from most previous studies of asphaltene thermodynamics, which were focused on the phase behavior of asphaltenes. Here, compositional gradients of asphaltenes, as well as the GOR of reservoir crude oils, are analyzed. Asphaltene gradients are shown to be strongly affected by both gravity and solubility. The latter effect is heavily dependent on the dissolved gas content of the reservoir crude oil. Case studies are provided that exhibit the power of this modeling.

Gas injection has been widely used for Improved Oil Recovery (IOR)/ Enhanced Oil Recovery (EOR) processes in oil reservoirs. Unlike the conventional gas injection (CGI) modes of CGI and Water Alternating Gas (WAG), the Gas-Assisted Gravity Drainage (GAGD) process takes advantage of the natural segregation of reservoir fluids to provide gravity stable oil displacement. It has been proved that GAGD Process results in better sweep efficiency and higher microscopic displacement to recover the bypassed oil from un-swept regions in the reservoir. Therefore, dry gas has been considered for injection in fractured basement reservoir, Bao Den (BD) oil field located in Cuu Long basin through the GAGD process application. This field, with a 5-year production history, has nine production wells and is surrounded by a strong active edge aquifer from the North-West and the South East flanks. The depth of basement granite top is about 2,800 mTVDss with a vertical oil column of 1,500m. The pilot GAGD project has been designed to test an isolated domain in the BD fractured basement reservoir where there is favorable reservoir conditions to implement GAGD. Both reservoir simulation and Lab test have been run and confirmed the feasibility and the benefit of GAGD project in the selected area.The Dry gas will be periodically injected through existing wellwith high water cut production that located in the isolated area. As the injected gas rises to the top to form a gas zone pushing GOC (gas oil contact) downward, and may push WOC (water oil contact) to lower part of this producer (or even away from bottom of the well bore) could lower down water cut when switch this well back to production mode. The matched reservoir model with reservoir and fluid properties have been used to implement sensitivity analysis, the result indicated that there is significantly oil incremental and water cut reduction by GAGDapplication. Many different scenarios have run to find the optimal reservoir performance through GAGD process. Among these runs, the optimal scenario, which has distinct target, requires high levels of gas injection rate to attain the maximum cumulative oil production.

Mud gas data from drilling operations provide the very first indication of the presence of hydrocarbons in the reservoir. It has been a dream for decades in the oil industry to predict reservoir gas and oil properties from mud gas data, because it would provide knowledge of the reservoir fluid properties in an early stage, continuously for all reservoir zones, and at low costs. Previous efforts reported in the literature did not lead to a reliable method for quantitative prediction of the reservoir fluid properties from mud gas data. In this paper, we propose a novel approach based on machine learning which enables us to predict gas oil ratio (GOR) from advanced mud gas (AMG) data. The current work is based on a previous successful pilot in unconventional (shale) reservoirs. Our aim is to extend the results of the pilot study to conventional reservoirs. In general, prediction of reservoir fluid properties is more challenging for conventional reservoirs than for unconventional reservoirs, due to the complexity of petroleum systems in conventional reservoirs. Instead of building a model directly from AMG data, we trained a machine learning model using a well-established reservoir fluid database with more than 2000 PVT samples. After thorough investigation of compositional similarity between PVT samples and AMG data, we applied the model developed from PVT samples to AMG data. The predicted GORs from AMG data were compared with GOR measurements from corresponding PVT samples to assess the accuracy of the GOR predictions. The results from 22 wells with both AMG data and corresponding PVT samples show large agreement between prediction vs. measurement. The accuracy of the predictive model is much higher than previous results reported in the literature. In addition, a Quality Check (QC) metric was developed to efficiently flag low-quality AMG data. The QC metric is vital to give confidence level for GOR prediction based on AMG data when PVT samples are not available. The study confirms that AMG data can be used as a new data source to quantitatively predict continuous reservoir fluid properties in the drilling phase. The method can be used to optimize wireline operations and for some cases, it provides a unique opportunity to acquire reservoir fluid data when conventional fluid sampling or use of wireline tools is not possible. After high-quality PVT data becomes available in the wireline logging phase, the continuous GOR prediction can be further improved and used to determine reservoir fluid gradient and reservoir compartmentalization.

Asphaltene gradient analysis in many wells in a large field match the Flory-Huggins-Zuo Equation of State (FHZ EoS) indicating equilibrated asphaltenes, thus reservoir connectivity. This analysis is consistent with data from over one year of production to date. Reservoir fluid samples were acquired with use of focused sampling techniques providing contamination free samples. Pressure measurements and many fluid properties are used to validate fluid equilibrium, including GOR and composition analyzed by the Cubic EoS, and a variety of markers in the condensate range, black oil range and the biomarker region. All analyses indicate equilibrium with the exception that the only two outliers in the asphaltene gradient curve are shown to be of different thermal maturity by utilizing the preferred biomarkers, the hopanes Ts and Tm. Moreover, the asphaltene abumdance in differently charged fluids varies by a factor of 6 while the maturity ratio Ts/(Ts+Tm) varies by 6% showing the sensitivity of asphaltene gradients for connectivity analysis. The modest levels of biodegradation (Peters- Moldovan rank=1) are used to constrain the petroleum system context of this reservoir considering that current reservoir temperatures significantly exceed biodegradation thresholds. There is evidence both that mildly biodegraded oil spilled into this reservoir and that some further biodegradation occurred in reservoir. Different gas-oil contacts in the field are associated with charge direction and show the limits of Cubic EoS for connectivity analysis in contrast to the good capability of the asphaltene gradients and FHZ EoS for this purpose. Moreover, the FHZ EoS analysis indicates that the asphaltenes are dispersed as a true molecular solution for this light oil in accord with the Yen-Mullins model of asphaltenes. Results from detailed whole-core and petrophysical analyses supports connectivity analysis. Core analysis shows the lack of any asphaltene deposition in the reservoir as expected from the fluid and asphaltene evaluations.

Asphaltenes can be dispersed in crude oils in 3 different forms; molecules, nanoaggregates (of molecules) or clusters (of nanoaggregates); these forms are codified in the Yen-Mullins model and relate to the extent of solvency of the asphaltenes in the crude oil. Many reservoir studies are used here to show the systematic behavior of the specific asphaltene species in crude oil and the corresponding magnitude of the asphaltene (and viscosity) gradients. In addition, the specific asphaltene species is related to the chemical origin controlling asphaltene onset pressure (AOP) and tar formation and depends on 1) the quality of the live crude oil solvent for asphaltenes and 2) the concentration of asphaltenes. Elevated quantities of solution gas of a reservoir crude oil significantly reduce the solvency of asphaltenes in crude oil. For low concentrations and/or good solvency, asphaltenes are dispersed in crude oils as molecules with small gradients (unless there are large GOR gradients). For moderate concentrations and/or moderate solubility, asphaltenes are dispersed as nanoaggregates with intermediate (gravity) gradients of asphaltenes. With large concentrations and/or poor solvency, asphaltenes are dispersed as clusters with very large gradients in reservoirs. These crude oils can also exhibit higher asphaltene onset pressures and/or phase separated bitumen or tar in the reservoir depending on the origin of asphaltene cluster formation. Secondary gas charge into oil reservoirs can yield tar and/or a high AOP. The effect of biodegradation on these factors is also discussed. The systematics presented here are helpful in understanding a variety of reservoir concerns associated with asphaltenes.

Reservoirs with multiple processes that impact the quality and distribution of crude oils can be complex. Here, a series of seven reservoirs in the North Sea contain a viscosity profile that is strongly affected by a spill-fill sequence of reservoir charging, biodegradation, water washing, and variation in thermal maturity. Mapping the viscosity gradient and its origins is critical to understand how each factor contributes to the complexity of these reservoirs. Additionally, evaluation of reservoir connectivity is key. Combined studies of downhole fluid analysis (DFA) and high-resolution compositional analysis by comprehensive two-dimensional gas chromatography (GC×GC) can be used to unravel the contributions of various effects and provide an improved understanding of the reservoir. The seven reservoirs examined are consistent with a simple description of the multiple processes that contribute to the viscosity gradient and connectivity analysis. The maturity of charge in this spill-fill sequence has continued to increase with time; consequently, crude oil in deepest reservoir is most mature and the shallowest, the least mature. The oil spills from the oil-water contact (in these injectite reservoirs); consequently, the deepest reservoir is the least biodegraded and the shallowest reservoir the most biodegraded. Observations here are consistent with both biodegradation and water washing being dominated by in-reservoir confinement of the oil as opposed to during migration. The deepest reservoir exhibits mild biodegradation and mild water washing, while the nearby shallowest reservoir exhibits severe biodegradation and severe water washing. Finally, in this spill-fill process, the replacement of new oil for old, especially in the deepest reservoirs, indicates excellent reservoir connectivity.

The evaluation of petroleum resources requires a good understanding of the reservoir fluids and their behavior. Specifically, PVT (pressure-volume-temperature) data are required at the earliest possible stage to develop economic evaluations, interpret well test data, design production equipment, and predict future reservoir performance. A fluid sample obtained with an openhole wireline formation tester tool provides the engineer with the first (and potentially the only) chance to analyze a representative reservoir fluid sample before significant decisions are made. The fluids must be characterized by direct measurement of certain properties and the quality of the PVT laboratory results depends almost entirely on the validity of the sample used for the analysis. This paper compares the fluid PVT data obtained using samples from wireline formation tester tools and from surface production test equipment. The PVT laboratory procedures and the accuracy of the analysis methods are discussed. The compositional analyses of the hydrocarbon samples are compared, from methane to heptanes-plus chemical structure, along with the key volumetric parameters such as density, bubblepoint pressure, viscosity, formation volume factor, and gas-oil ratio. Different types of reservoirs (with fluids ranging from low gas-oil ratio oils to dry gas) were examined to determine the effects of mud filtrate invasion in the openhole environment, type of fluid, and reservoir characteristics. In general, the results indicate that samples obtained with wireline formation tester tools can provide representative reservoir fluid parameters from compositional analyses to basic volumetric behavior as a function of pressure and temperature. Critical reservoir parameters such as the fluid density, bubblepoint pressure, formation volume factor, viscosity, and gas-oil ratio are consistent within about 15 percent for most oil reservoir fluids. Gas condensate reservoir fluids present the most difficult sampling conditions for both the downhole and surface sampling operations. Careful sampling techniques and detailed monitoring of the downhole and surface fluid conditions are required to reconstruct useful results for the reservoir fluid properties.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 181526, “New Wireline, In-Situ, Downhole Fluid Compositional Analyses To Enhance Reservoir Characterization and Management,” by Gabor Hursan and S. Mark Ma, Saudi Aramco, and Wael Soleiman, Sami Eyuboglu, Neeraj Sethi, and Nacer Guergueb, Halliburton, prepared for the 2016 SPE Annual Technical Conference and Exhibition, Dubai, 26–28 September. The paper has not been peer reviewed. This study focuses on recent experience in Saudi Arabia with crude-oil compositional analyses during pumpout with a wireline formation tester (WFT). It summarizes experience with the in-situ measurement of methane, ethane, propane, saturates, aromatics, and gas/oil ratio (GOR) on the basis of multivariate optical computing (MOC) conducted at more than 200 pumpout stations in a total of 37 wells drilled with a variety of inclinations, bit sizes, and drilling fluids in several oil and gas fields. Introduction In reservoir-fluid characterization performed in the laboratory conventionally, samples of representative formation fluids are analyzed to determine bulk fluid properties, fluid-phase behavior, and chemical properties. Exploration and evaluation wells are often drilled exclusively for fluid-analysis purposes for which the only way to analyze or capture formation fluids is a downhole pumpout WFT (PWFT). Capturing high-quality reservoir samples is one of the most important objectives in any PWFT job. The keys to ensure fluid cleanup during pumpout are (1) a set of downhole sensors measuring the pumped fluids and (2) the unambiguous contrast between the drilling-mud filtrate and reservoir fluids as measured by at least one of these sensors. Avoiding such fluid ambiguity is a challenge in many situations. Optical sensors respond with high sensitivity to chemical compositions of fluids. Implemented downhole, these tools can provide a powerful means of differentiating between the oil-based-mud (OBM) filtrate and reservoir oils. The most valuable benefit of these measurements is the prompt availability of in-situ fluid-composition data without the additional steps of acquiring, transporting, and analyzing physical fluid samples in the laboratory. This process requires durable, robust, and accurate optical sensors that operate reliably and consistently in the hostile downhole environment. One recently developed optical-sensor system is based on an optical device known as an integrated computational element (ICE) that performs the mathematical operation of MOC. For each fluid component, an ICE “core” is engineered such that only one particular fluid component or property is accentuated in the detector response and everything else is muted. This detector response is then used to infer the abundance of the fluid component of interest. These optical elements are typically very broadband and may have a response that extends from 400 to 5000 nm. The high bandwidth of these optical elements combined with their intrinsic high signal/noise ratio enables laboratory-grade optical analysis downhole.

The variation of temperature profile of the sandface and wellbore offers important indicators for the occurrence of various downhole phenomena, as functions of reservoir formation and fluid properties. This paper aims to investigate the assimilation of temperature and production data for robust and efficient estimation of reservoir formation properties and fluid characterization. Very few previous work in the literature used Distributed Temperature Sensing (DTS) data for the characterization of the reservoir fluid and geostatistical description. The main contribution of this work lies in the proposed methodology for integration of temperature data from DTS systems to obtain a robust estimation of the reservoir fluid description, as well as introducing the integration of temperature data as an alternative and/or complementary data for integrative reservoir characterization. Two synthetic reservoir examples are investigated. In the first example, we focus on resolving the geostatistical description of a 3D multi-phase reservoir under water flooding. An ensemble-based data assimilation algorithm is used to compare the characterization of reservoir formation porosity and permeability by integration of temperature profile and well production data as opposed to the integration of production logging tool (PLT) data. Our results show that the DTS temperature data shows good potential as an alternative to the PLT data for reservoir rock characterization, although there may be a risk of model overfitting due to the large amount of data. In the second example, we attempt to estimate the original composition of the reservoir fluid as well as reservoir rock properties using downhole temperature profile data. A relatively simple multi-layer radial reservoir model is considered in this case. However, in order to honor the physical bounds on the various model parameters, a constrained ensemble-based data assimilation algorithm based on Lagrange multipliers is adopted. The computational results show that the original reservoir fluid composition could be excellently estimated for a case with known reservoir properties. Presence of uncertainty in the reservoir formation properties resulted in an poor estimation of the original reservoir fluid composition.

Joslin, W.J., Member AIME, Creole Petroleum Corp., Caracas, Venezuela Abstract The frontal-advance equation can determine how the fluid withdrawal rate and subsurface operating pressure influence oil recovery from pressure-maintained reservoirs having characteristics favorable for efficient vertical segregation. For effective vertical segregation in formations having low dip angles, the requirements are: thick, massive sands, low viscosity oil and high vertical permeability with few discontinuous shale members. This approach has been successfully applied to the LL-370, which is a massive Eocene sandstone reservoir containing crude with an average gravity of 260 API, a dip of only 30 and vertical permeability of 1/2 darcy. The average gas cap saturation was calculated for various rates of vertical gas-oil contact movement corresponding to a wide range of reservoir producing rates. This method also evaluated the effect of different pressure maintenance levels on oil recovery. Applicability of this procedure for calculating the present oil saturation in the LL-370 gas cap has been substantiated from field performance, where the oil saturation was computed by comparing oil recovery with the gas cap volume vacated. Introduction During the natural depletion history of the LL-370 reservoir, located in Lake Maracaibo, Venezuela, excellent vertical segregation resulted in the formation of a thin gas zone over a substantial portion of the oil-productive area. Subsequently, gas injection forced this thin gas-saturated zone slowly downward, reducing the oil saturation in the gas cap to a low value. The oil recovery has been raised considerably above earlier predictions where vertical segregation effects were under-estimated. In order to determine the maximum efficient producing rate and optimum operating pressure for this reservoir, a vertical gas frontal-advance analysis has been made through application of the fractional flow equation. LL-370 RESERVOIR CHARACTERISTICS The reservoir consists of a truncated monocline of massive, highly-permeable Eocene sandstone, called the B-6 formation, covering an area of more than 13,500 acres. The original oil in place was 2.17 billion STB. Internal non-sealing faults subdivide the structure into four blocks called the B-6-x.6, 10, 11 and 13, which are effectively bounded by major faulting on the flanks, an unconformity updip to the west, and an immobile aquifer to the southeast. The producing formation occurs at an average depth of 5,250 ft subsea and has a closure of more than 1,500 ft. A typical producing section has a net oil sand thickness of 170 ft. Scattered throughout the section are a few randomly-positioned shale lenses which increase in thickness and areal extent downdip. The reservoir rock and fluid properties vary with depth. The permeability and produced oil gravity are 1600 md and 280 API at the top, compared to 300 md and 18 API at the water-oil contact. The productivity of updip wells is, therefore, much higher than the reservoir average. The reservoir oil was originally saturated at the crest, grading to a slight degree of undersaturation at the water-oil contact. There was no original gas cap; and material balance and the field performance indicate no water influx. Except for the northern B-6-x.6 block, the fluid and rock properties have already been published. Average LL-370 reservoir properties are shown in Table l. RESERVOIR PERFORMANCE UNDER NATURAL DEPLETION Fig. 1 shows production performance from 1939 to date. TABLE 1-LL-370 RESERVOIR PROPERTIES Permeability (air) from cores, horizontal 975 mdPorosity 20.4 per centGravity 26.1 APIFormation volume factor, original 1.278Connate water saturation 12.4 per centSolution GOR, original 492 cu ft/bblOil viscosity, original 1.8 cpFormation dip 3Original pressure (-5250 ft subsea) 2490 psicSection thickness 198 ftNet oil sand thickness 148 ft JPT P. 87ˆ

The controls on the composition of biodegraded oils in the deep subsurface – Part 3. The impact of microorganism distribution on petroleum geochemical gradients in biodegraded petroleum reservoirs

Arps in 1944 developed decline curve equations for analyzing reservoir/well production decline and the estimated decline rate constant is used for production forecast. In making reservoir production forecast, the Arps equations are usually calibrated to match the reservoir historical production decline by adjusting the decline rate constant value. The decline rate constant indirectly influences the reservoir properties responsible for production decline. However, the value assigned to the decline rate constant might either overestimate or underestimate the uncertainties in the reservoir rock and fluid properties responsible for reservoir production decline. The decline rate constant in the Arps equation has no equation relating it to the reservoir properties that are responsible for the reservoir production decline hence; the direction of uncertainty quantification on these reservoir parameters is not explicit, therefore undermining the predictive power of the Arps equations. The impact of reservoir properties uncertainties on the production rate decline and forecast by Arps equations cannot be examined due to the empirical nature of the Arps equations. Also, the decline rate constant is not explicitly related to reservoir rock and fluid properties responsible for production decline. However, the predictive power of the Arps model can be greatly improved if the decline constant can be expressed as a function of the reservoir rock and fluid properties responsible for production decline. Hence, the impact of uncertainty on the production forecast by Arps models can be examined. The objective of this work is to introduce reservoir rock and fluid properties into the Arps decline curve equations. This will enable the imposition of reservoir physics on the decline curve equations and the impact of uncertainties in these properties on matching historical production decline and forecast. In addition, a relationship between the decline rate constant in the Arps equations and the reservoir rock and fluid properties that influence reservoir production decline has been developed. A mechanistic approach using dimensional analysis was used to develop the relationship. The decline rate was assumed to be proportional to the various reservoir rock and fluid parameters, such as permeability, porosity, net-to-gross, drainage area, fluid density, and viscosity, that could influence production decline and by unit dimensional analysis, a relationship was developed between the decline constant and these reservoir rock and fluid properties. The decline rate constant was directly related to the product of reservoir permeability, fluid density, and the square of the pressure difference but inversely proportional to the cubic power of fluid viscosity. The relationship developed for the decline constant in this work will give the modified Arps model strong predictive power and allows for the impact of uncertainties on the production forecast to be evaluated. The results obtained from the modified Arps equations give better reservoir properties dependent outcomes than the traditional Arps equations and enable analysis of the effects of reservoir rock and fluid properties uncertainties on the production decline and forecast.

A major factor in the success of a miscible hydrocarbon enhanced oil recovery process is the phase behaviour relationship between the injection and reservoir fluids. This paper describes the experimental procedures developed at Gulf Canada R&D to generate phase equilibrium data for specific reservoirs. The data have been applied in evaluating the potential for enhanced recovery in a number of Western Canadian carbonate reservoirs. The over-all procedure for a complete phase equilibrium study is described. This comprises field sampling, reservoir fluid characterization, injection fluid preparation, development of phase envelopes and correlation of phase behaviour. An integral part of the program involved the development Of a combination of analytical and high-pressure techniques to facilitate the rapid accumulation of vapour-liquid equilibrium data for reservoir-injection fluid systems. The Peng-Robinson equation of state and a new K value correlation were used successfully to rationalize the measured data. Experimental phase behaviour measurements in conjunction with appropriate correlation methods have proved valuable in tailoring the design of injection fluids to meet both miscibility requirements and economic constraints. Introduction Recent reports(1,2) have indicated that the use of enhanced recovery methods can increase Canada's available conventional oil reserves by approximately 2 to 3 billion barrels. Up to 95% of the additional oil with an API gravity greater than 25º could feasibly, and economically, be recovered using currently available miscible displacement techniques involving injection into the reservoir of either CO2 or hydrocarbon fluids. Realizing this potential, Gulf Canada Resources Inc. have been actively pursuing a program of initiating limited full-scale enhanced recovery projects and implementing pilot-scale floods(3) at various locations in Western Canada. To date, because of the types of reservoirs considered primarily carbonate and the local availability of suitable injection fluid components, attention has centred on the process of hydrocarbon miscible displacement. In support of these projects, the Gulf Canada R&D Department has developed an extensive experimental program to provide data both for preliminary screening of candidate reservoir and injection fluids and for detailed design of optimum injection fluids in specific reservoirs. This paper describes the experimental program that has evolved during application to such projects over the past five years. Miscible Displacement The residual oil saturation, after primary and secondary production of a reservoir, is a function of the formation geometry and the interfacial tensions of the fluids occupying the formation. The purpose of the injection fluid in enhanced recovery is to access the residual oil, and, by modifying the balance of interfacial tensions, to mobilize the residual oil to production. In miscible displacement processes, as the name implies, this purpose is achieved when miscibility is attained between the displacing fluid (injection fluid) and the residual oil, thereby eliminating the interfacial tension between the two. The miscibility of an injection fluid and reservoir oil is governed by the phase behaviour which is unique to each specific combination. The relationship between phase behaviour, miscibility and composition at reservoir temperature and pressure is illustrated on the pseudo-ternary Point R is the reservoir fluid composition and X represents the critical composition or plait point.