William D. McCain

Investigation of Well Productivity in Gas-Condensate Reservoirs

The productivity of the wells in a moderately rich gas condensate reservoir was observed to initially decrease rapidly and then increase as the reservoir was depleted. All wells in the field showed the same response. Compositional simulation explained the reasons for these productivity changes. During early production, a ring of condensate rapidly formed around each wellbore when the near-wellbore pressures decreased below the dew point pressure of the reservoir gas. The saturation of condensate in this ring was considerably higher than the maximum condensate predicted by the PVT laboratory work due to relative permeability effects. This high condensate saturation in the ring severely reduced the effective permeability to gas, thereby reducing gas productivity. After pressure throughout the reservoir decreased below the dew point condensate formed throughout the reservoir, thus the gas flowing into the ring became leaner causing the condensate saturation in the ring to decrease. This increased the effective permeability of the gas. This caused the gas productivity to increase as was observed in the field. There were also changes in gas and condensate compositions in the reservoir which affected viscosities and densities of the fluids. These effects also impacted gas productivity. This work is another step forward in our understanding of the dynamics of condensate buildup around wellbores in gas condensate fields.

Reservoir oil bubblepoint pressures revisited; solution gas-oil ratios and surface gas specific gravities

Abstract A large number of recently published bubblepoint pressure correlations have been checked against a large, diverse set of service company fluid property data with worldwide origins. The accuracy of the correlations is dependent on the precision with which the data are measured. In this work a bubblepoint pressure correlation is proposed which is as accurate as the data permit. Certain correlations, for bubblepoint pressure and other fluid properties, require use of stock-tank gas rate and specific gravity. Since these data are seldom measured in the field, additional correlations are presented in this work, requiring only data usually available in field operations. These correlations could also have usefulness in estimating stock-tank vent gas rate and quality for compliance purposes.

Producing Rich-Gas-Condensate Reservoirs--Case History and Comparison Between Compositional and Modified Black-Oil Approaches

This paper presents the results of a full field simulation study for a rich gas condensate reservoir with complex fluid behavior. Unique to this paper is a comparison between Modified Black-Oil (MBO) and compositional simulation in a full field model with water influx. Geological, petrophysical, fluid properties, rock-fluid properties, and well data were used to build two full field simulation models (14-component Equation-of-State, compositional model and 3-component MBO model). More than 14 months of daily gas, oil, and water production and tubing pressure data from 4 wells were matched using the MBO model. The model was then used to forecast production and identify new development locations. Comparison runs between the MBO and the fully compositional models were made. It was found that the two models agreed for the entire simulation above and below the dew point and with water influx from the aquifer. The MBO runs were at least 5 times faster than the most efficient compositional run. The use of the MBO approach allowed a rapid history match of the field performance and a timely completion of the simulation study. Contrary to the common belief that a compositional simulation approach is needed for modeling near-critical reservoirs, this study shows that a MBO approach can be used instead of a fully compositional approach for modeling depletion and water influx processes in near-critical reservoirs. This approach may result in significant time saving in full field simulation.

Correlation of Bubblepoint Pressures for Reservoir Oils--A Comparative Study

None of the currently proposed correlations for bubblepoint pressure are particularly accurate. Knowledge of bubblepoint pressure is one of the important factors in the primary and subsequent developments of an oil field. Bubblepoint pressure is required for material balance calculations, analysis of well performance, reservoir simulation, and production engineering calculations. In addition, bubblepoint pressure is an ingredient, either directly or indirectly, in every oil property correlation. Thus an error in bubblepoint pressure will cause errors in estimates of all oil properties. These will propagate additional errors throughout all reservoir and production engineering calculations. Bubblepoint pressure correlations use data which are typically available in the field; initial producing gas-oil ratio, separator gas specific gravity, stock-tank oil gravity, and reservoir temperature. The lack of accuracy of current bubblepoint pressure correlations seems to be due to an inadequate description of the process – in short, one or more relevant variables are missing in these correlations. We considered three independent means for developing bubblepoint pressure correlations. These are (1) non-linear regression of a model (traditional approach), (2) neural network models, and (3) non-parametric regression (a statistical approach which constructs the functional relationship between dependent and independent variables, without bias towards a particular model). The results, using a variety of techniques (and models), establish a clear bound on the accuracy of bubblepoint pressure correlations. Thus, we have a validation of error bounds on bubblepoint pressure correlations.

The Eagle Ford Shale Play, South Texas: Regional Variations in Fluid Types, Hydrocarbon Production and Reservoir Properties

The Eagle Ford Shale is one of the most active U.S. shale plays; it produces oil, gas condensate, and dry gas. To better understand the regional and vertical variations of reservoir properties and their effects on fluid types and well performance, we conducted an integrated, regional study using production and well log data. Maps of the average gas-oil ratio (GOR) of the first three production months identified four fluid regions, including black oil, volatile oil, gas condensate, and dry gas regions. Maximum oil production occurs in Karnes County, where first-month oil production of most wells exceeds 5,000 barrels (bbl). The most productive gas region is between the Stuart City and Sligo Shelf Margins, where first-month gas production of most wells exceeds 60 million cubic feet (MMcf). Eagle Ford Shale petrophysical properties were analyzed in individual wells and were mapped to clarify the regionally variations of Eagle Ford Shale reservoir properties and their controls on fluid types and well performance. In comparison to the upper Eagle Ford, the lower Eagle Ford Shale has high gamma ray, high resistivity, low density, and long transit time values; we infer that the lower Eagle Ford shale has higher total organic carbon and lower carbonate content than the upper Eagle Ford Shale. Integration of production and geological data shows that thermal maturity and structural setting of the Eagle Ford Shale strongly influence fluid types and production rates. Plots of GOR vs. time for individual wells were constant in different reservoir fluids. Results of this study clarify causes of vertical and lateral heterogeneity in the Eagle Ford shale and the regional extents of fluid types. Understanding of the reservoir property differences between upper and lower Eagle Ford Shale should assist with optimizing completion design and stimulation strategies. The results may be applicable to similar developing shale plays.

More Accurate Gas Viscosity Correlation for Use at HP/HT Conditions Ensures Better Reserves Estimation

High-pressure and high-temperature (HPHT) gas reservoirs are defined as having pressures greater than 10,000 psia and temperatures over 300oF. Modeling the performance of these unconventional reservoirs requires the understanding of gas behavior at elevated pressure and temperature. An important fluid property is gas viscosity, as it is used to model the gas mobility in the reservoir that can have a significant impact on reserves estimation during field development planning. Accurate measurements of gas viscosity at HPHT conditions are both extremely difficult and expensive. Thus, this fluid property is typically estimated from published correlations that are based on laboratory data. Unfortunately, the correlations available today do not have a sufficiently broad range of applicability in terms of pressure and temperature, and so their accuracy may be doubtful for the prediction of gas viscosity at HPHT conditions. This paper reviews the databases of hydrocarbon gas viscosity that are available in the public domain, and discusses the validity of published gas viscosity correlations based on their applicability range. A falling body viscometer was used in this research to measure the HPHT gas viscosity in the laboratory. The instrument was calibrated with nitrogen and then, to represent reservoir gas behavior more faithfully, pure methane was used. The subsequent measured data, recorded over a wide range of pressure and temperature, was then used to evaluate the reliability of the most commonly used correlations in the petroleum industry. The results of the comparison are presented here and suggest that at pressures higher than 8000 psia; the laboratory measurements drift from the National Institute of Standards and Technology (NIST) values by up to 7.48%. Finally, a sensitivity analysis was performed to assess the effect of gas viscosity estimation errors on the overall gas recovery from a synthetic HPHT reservoir, using numerical reservoir simulations. The result shows that a -10 % error in gas viscosity can produce an 8.22% error in estimated cumulative gas production, and a +10% error in gas viscosity can lead to a 5.5% error in cumulative production. These preliminary results indicate that the accuracy of gas viscosity estimation can have a significant impact on reserves evaluation.

Applications of the Coefficient of Isothermal Compressibility to Various Reservoir Situations With New Correlations for Each Situation

Summary The coefficient of isothermal compressibility (oil compressibility) is defined as the fractional change of oil volume per unit change in pressure. Though the oil compressibility so defined frequently appears in the partial-differential equations describing fluid flow in porous media, it is rarely used in this form in practical engineering calculations. Instead, oil compressibility is usually assumed to be constant, allowing the defining equation to be integrated over some pressure range of interest. Thus, the oil compressibility in the resulting equations should be regarded as a weighted average value over the pressure range of integration. The three distinct applications for oil compressibility in reservoir engineering are (1) instantaneous or tangent values from the defining equation, (2) extension of fluid properties from values at the bubblepoint pressure to higher pressures of interest, and (3) material-balance calculations that require values starting at initial reservoir pressure. Each of these three applications requires a different approach to calculating oil compressibility from laboratory data and in developing correlations. The differences among the values required in these three applications can be as great as 25%. Most published correlations do not indicate the particular application to which the proposed correlation applies. A correlation equation for oil compressibility has been developed using more than 3,500 lines of data from 369 laboratory studies. This correlation equation gives the average compressibility between the bubblepoint pressure and some higher pressure of interest. Equations to calculate appropriate values of compressibility for the other two applications are presented.

Determination of Bubble-Point and Dew-Point Pressure without a Visual Cell

Bubble-point and dew-point pressures of oil and gas condensate reservoir fluids are used for planning the production profile of these reservoirs. Usually the best method for determination of these saturation pressures is by visual observation when a Constant Mass Expansion (CME) test is performed on a sample in a high pressure cell fitted with a glass window. In this test the cell pressure is reduced in steps and the pressure at which the first sign of gas bubbles is observed is recorded as bubble-point pressure for the oil samples and the first sign of liquid droplets is recorded as the dew-point pressure for the gas condensate samples. The experimental determination of saturation pressure especially for volatile oil and gas condensate require many small pressure reduction steps which make the observation method tedious, time consuming and expensive. In this study we have extended the Y-function which is often used to smooth out CME data for black oils below the bubble-point to determine saturation pressure of reservoir fluids. We started from the initial measured pressure and volume and by plotting log of the extended Y function which we call the YEXT function, with the corresponding pressure, two straight lines were obtained; one in the single phase region and the other in the two phase region. The point at which these two lines intersect is the saturation pressure. The differences between the saturation pressures determined by our proposed YEXT function method and the observation method was less than 4.0 % for the gas condensate, black oil and volatile oil samples studied. This extension of the Y function to determine dew-point and bubble-point pressures was not found elsewhere in the open literature. With this graphical method the determination of saturation pressures is less tedious and time consuming and expensive windowed cells are not required.

Measurement of Gas Viscosity at High Pressures and High Temperatures

Gas viscosity is an important fluid property in petroleum engineering due to its impact in oil and gas production and transportation where it contributes to the resistance to the flow of a fluid both in porous media and pipes. Although the property has been studied thoroughly at low to intermediate pressures and temperatures, there is lack of detailed knowledge of gas viscosity behavior at high pressures and high temperatures (HPHT) in the oil and gas industry. The need to understand and be able to predict gas viscosity at HPHT has become increasingly important as exploration and production has moved to ever deeper formations where HPHT conditions are more likely to be encountered. Knowledge of gas viscosity is required for fundamental petroleum engineering calculations that allow one to optimize the overall management of a HPHT gas field and to better estimate reserves. Existing gas viscosity correlations are derived using measured data at low to moderate pressures and temperatures, i.e. less than 10,000 psia and 300 oF, and then extrapolated to HPHT conditions. No measured gas viscosities at HPHT are currently available, and so the validity of this extrapolation approach is doubtful due to the lack of experimental calibration. The falling body viscometer is selected to measure gas viscosity for a pressure range of 3,000 to 24,500 psia and temperature range of 100 to 415 oF. Nitrogen was used to calibrate the instrument and to account for the fact that the concentrations of non-hydrocarbons are observed to increase dramatically in HPHT reservoirs. Then methane viscosity is measured to reflect the fact that, at HPHT conditions, the reservoir fluids will be very lean gases, typically methane with some degree of impurity. The experiments showed that while the correlation of Lee et al. accurately estimates gas viscosity at low to moderate pressure and temperature, it does not provide a good match to gas viscosity at HPHT conditions.

Sampling Volatile Oil Wells

Recombined surface samples are usually used for volatile oil laboratory fluid property studies. A procedure for stabilizing and surface sampling of volatile oil wells is currently used in the industry. However, no investigation of the quality of the samples resulting from this procedure has ever been published. Typically, during surface sampling, bottom-hole flowing pressure is less than the bubblepoint pressure of the original reservoir oil. This causes gas to form in a cylinder of the reservoir around the wellbore. Understanding the dynamics of this cylinder of gas saturation is critical to obtaining a recombined surface sample representative of original reservoir oil. It is possible to obtain a representative sample if this cylinder is stable. This paper presents the results of a study of the sampling procedure. The effects of production rate prior to and during the sampling process were quantified using radial compositional simulation. The buildup and stability of the ring of gas saturation were examined. Guidelines for sampling volatile oil wells is presented. It is based on comparisons of the compositions of recombined surface samples with the compositions of original reservoir oils for various producing schemes. These guideline are expected to give the best chance of obtaining a representative sample from a volatile oil well. Introduction Several authors discussed resevoir fluid sampling. A study on gas condensate reservoir sampling has recommended that sampling should be done early in the life of the reservoir. The usual procedure of reducing the rate before sampling may be useful in increasing the chance of obtaining a valid fluid sample in gas condensate reservoirs. In this paper, we used compositional reservoir simulation to investigate sampling in volatile oil wells. Simulation Model We used a radial compositional simulation model to investigate the changes in composition for volatile oils and to understand the effect of these changes on fluid sampling. The results reported here are those obtained for fluid sample “Oil 2” of Coats and Smart. PVT Modeling. We used an EOS model to match the PVT behavoir of a volatile oil sample. The iso and normal components for C4 and C5 were lumped together and the C7+ fraction was split into three components using the Whitson’s method. This resulted in an eleven-component fluid system. We then used the Peng and Robinson EOS to match the PVT data of the fluid sample. Following Coats and Smart procedure, We used ΩA, ΩB for C1 and the three heavy components, accentric factors for the three heavy components, and the binary interaction coefficients for the three heavy components with C1 as regression variables. The match with the laboratory data was satisfactory. Figs. 1-3 show the match between some simulated and actual PVT properties for differential liberation and constant volume depletion (CVD) data. Radial Compositional Model. We constructed a radial simulation model and used it to investigate the near-wellbore compositional changes. The model had twenty-two grid blocks in the radial direction. The block sizes increased logarithmically from 0.5 ft (the wellbore) to 100 ft. and then uniforamlly to a reservoir radius of 1490 ft. (160 acres). Gasoil relative permeability are shown in Fig. 4. Other reservoir and fluid data for the base case are given in Table 1. Simulation Results Several runs were made to investigate the compositional changes that can occur at different production rates and to study the effect of the common procedure of reducing the production rate before sampling. In the following sections, we discuss the results of our compositional simulation experiments for five different cases. These cases show the SPE 67232 Sampling Volatile Oil Wells Ahmed H. El-Banbi, SPE, Cairo University/Schlumberger Holditch-Reservoir Technologies, and William D. McCain, Jr., SPE, Texas A&M University 2 A. H. EL-BANBI AND W.D. MCCAIN, JR. SPE 67232 effect of producing the at high rate, producing at low rate, reducing the rate from the high rate case before sampling, reducing the rate from the low rate case before sampling, and shutting-in the well before sampling. We used the mole fraction of C7+ 11 in the well stream as indicator of compositional changes between the recombined surface sample and the original reservoir fluid. Effects of compositional changes are also reported. Case 1: Production at High Rate. The well was produced at high rate of 1,000 STB/D. After 220 days of production at the high rate, the well could not maintain its rate because it reached a minimum bottom-hole pressure of 1,470 psia. The average field pressure, first model block pressure, and well bottom-hole flowing pressure are shown in Fig. 5. The change in slope of the field average pressure shows that the bubble point pressure was reached around 50 days. Accordingly, an increase in the producing gas-oil ratio (GOR) can be seen after 70 days of production (Fig. 6). Because of the high production rate, the GOR increased to very high levels. Fig. 7 shows the mole fraction of C7+ versus time. The original fluid C7+ mole fraction is also indicated on the plot. The figure shows that the C7+ mole fraction in the well stream is nearly the same as the original fluid C7+ mole fraction for at least the first 50 days of production. This suggests that a fluid sample taken early in the life of the reservoir (even when the bottom-hole pressure is slightly less than the bubble point pressure) will almost represent the original reservoir fluid. The sample will not be representative after depletion occurs in the reservoir. Gas saturation builds up near the wellbore and in the reservoir as pressure declines (Fig. 8). The gas saturation can build up immediately around the wellbore if the bottom-hole pressure around the wellbore is less than the bubble-point pressure. This gas saturation reduces the relative permeability to oil and increases the relative permeability to gas, reducing the oil productivity index. Case 2: Production at Low Rate. We produced the well at a lower rate this time (500 STB/D). This case has similar results to Case 1 except for the effect of lowering the pressure below the bubble-point pressure is delayed. Fig. 9 shows the C7+ mole fraction for the produced well stream. Although the pressure near the wellbore goes immediately below the bubble-point pressure (and gas saturation builds up), there is a better chance of obtaining a representative sample than the case of high production rate. Other simulation runs, at even lower rates, supported this observation. Case 3: Reducing the High Production Rate Before Sampling. In this case, the production rate was reduced from 1,000 STB/D to 200 STB/D after 180 days of production. Fig. 10 shows the average reservoir pressure, bottom-hole flowing pressure, and the first simulation cell pressure. The near wellbore pressure is affected by the reduction in production rate. At 180 days, the near wellbore pressure jumps to around 3,800 psia and shows a more gentle decline at production rate of 200 STB/D. The effect of reducing the oil production rate can be also seen as sudden decrease in the producing GOR (Fig. 11). The GOR will go back to its normal increasing trend after the production rate is stabilized at 200 STB/D. Fig. 12 (mole fraction of C7+) shows that when the well production rate is suddenly decreased, a spike of C7+ can be detected in the well stream. A fluid sample taken at this time will not be representative of the reservoir fluid. Fig. 13 shows the gas saturation developing near the wellbore and far in the reservoir. The figure indicates that the gas saturation around the wellbore will be affected by the reduction of rate. Case 4: Reducing the Low Production Rate Before Sampling. In this case, the production rate was reduced from a low rate of 500 STB/D to a lower rate of 200 STB/D. Fig. 14 is the C7+ mole fraction for the well stream fluid. At 180 days, the spike can be seen but with a lower magnitude when compared with Case 3 (Fig. 12). This suggests that production at low rate is desirable if a representative fluid sample is to be obtained. Case 5: Shut-in Before Sampling. This case shows the effect of shutting-in the well before fluid sampling. The simulation model was run at production rate of 1,000 STB/D for 30 days, followed by a shut-in period for 10 days, then produced again at a reduced rate of 200 STB/D. Fig. 15 shows the behavoir of C7+ mole fraction. The figure indicates that shutting the well in before sampling has a minimal effect on the quality of the sample. Discussion Obtaining a representative fluid sample is important to estimate the fluid PVT properties. These PVT properties are essential to almost all reservoir and production engineering calculations. Fluid sampling of volatile oil wells can be affected by the conditions of the well before sampling. In general, fluid samples should be taken before considerable depletion occurs in the reservoir. Ideally, the fluid sample will be representative of the original reservoir fluid if the pressure (both in the reservoir and near the wellbore) is not allowed to drop below the bubble-point. If the near wellbore pressure goes below the bubble point, a representative sample may still be obtained. However, if the reservoir pressure drops below the bubble-point, the fluid sample will not be representative of the original reservoir fluid. Compositional Changes. In volatile oil reservoirs, compositional changes affect the production behavoir. We used Case 2 simulation to show some of these effects. Fig. 16 compares the relative permeability in the first grid block for oil and gas. Oil relative permeability goes down with time while gas relative permeability goes up. This is a direct result of the saturation changes occuring near the wellbore with production. With in the increase in gas saturation, more gas SPE 67232 SAMPLING VOLATILE OIL WELLS 3 passes into the wellbore. As a result, pr