A NEW APPROACH TO OBTAIN IN-SITU LIVE FLUID COMPRESSIBILITY IN FORMATION TESTING

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1 A NEW APPROACH O OBAIN IN-SIU LIVE FLUID COMPRESSIBILIY IN FORMAION ESING Li Chen, Adriaan Gisolf, Beatriz E. Barbosa, Julian Youxiang Zuo, Vinay K. Mishra, Hadrien Dumont, homas Pfeiffer, Vladislav Achourov Copyright 2014, held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors. his paper was prepared for presentation at the SPWLA 55th Annual Logging Symposium held in Abu Dhabi, United Arab Emirates, May 18-22, ABSRAC Compressibility and density are important fluid properties that are used in dynamic reserve calculations such as the material balance equation and reservoir simulation. Compressibility is normally obtained from pressure-volumetemperature (PV) measurements performed in the laboratory. However, for samples obtained at reservoir pressures exceeding 15,000 psi, extrapolation techniques are sometimes used, introducing uncertainty in the calculated results. A new approach has been developed to obtain compressibility from downhole fluid analysis measurements up to 25,000 psi. A formation testing tool pumps formation fluid from the reservoir. Pressure and density of the pumped fluid are measured in the flowline of the tool. A change in pressure may be induced by a change in pump rate and/or by closing valves in the tool. hese dynamic pressure and density data are used to calculate compressibility. When the density sensor is placed between the formation interface module and the pump, density is measured at and below formation pressure. In this scenario, no extrapolation is required to derive compressibility in situ at reservoir conditions. It is possible to place the density sensor downstream of the pump. Density is then measured at and above mud pressure. he obtained pressure-density cross plot can be used, not only to derive the fluid compressibility, but also to extrapolate the density and compressibility to reservoir pressure or, if desired, to saturation pressure. he measurement of pressure and density, the compressibility calculation, and the density extrapolation are all performed in real time during data acquisition with the tool in the well. his method has been successfully applied in Gulf of Mexico and other deep-water wells for various fluid types. he presented data examples cover high pressure (>20,000 psi) environments. he calculated compressibility and measured or extrapolated density values are validated by laboratory measurements for the lower pressure examples. Additionally, a best practice has been developed for various formation testing tool configurations to maximize the quality of the obtained compressibility data. INRODUCION Availability of accurate fluid properties (McCain, 1990) is critical to the success of reservoir engineering processes such as reserve estimation, production potential and design, field development planning, and flow assurance. Fluid properties are routinely determined from laboratory measurements performed on fluid samples. Such samples can be obtained with, for example, wireline formation testers, formation samplingwhile-drilling tools, bottom hole drill stem test (DS) sampling tools, or separator samples. oday, many fluid properties, such as (limited range) composition, gas-oil ratio (GOR), density, and viscosity can also be obtained downhole in real time with formation tester fluid analyzers (Mullins, 2008; Achourov et al., 2011). here are many reasons that real-time availability of fluid properties is important: fluid DFA and sampling programs can be optimized, Fluid grading studies can be performed and downhole fluid analysis (DFA) can be a critical input to reservoir compartmentalization studies. Furthermore, decisions about the reservoir, the well, completion, and production can be made based on DFA before laboratory analysis is available. DFA measurements can also be used in the absence of the laboratory measurements, when fluid samples are not available or, in some cases, when reservoir condition exceeds the laboratory pressure or temperature limits. his paper will discuss a new 1

2 method to determine isothermal fluid compressibility downhole. Fluid compressibility is used in dynamic reserve estimation and in assessment of production potential through different drive mechanisms. Compressibility is a function of pressure. For compressible fluids such as hydrocarbons above saturation pressure, compressibility changes continuously with pressure in a nonlinear fashion. herefore, compressibility measurements are performed over a range of pressures at constant temperature. ypically the reservoir temperature is chosen. COMPRESSIBILIY FROM LABORAORY MEASUREMEN Isothermal compressibility is historically determined from constant composition expansion (CCE) experiments conducted in the laboratory. CCE experiments are performed by placing a fluid sample in a visual pressure-volume-temperature (PV) cell at constant temperature. Incremental pressure changes are induced, and the change in fluid volume is measured at each pressure step. As long as the pressure remains above the fluid saturation pressure, the isothermal compressibility can be derived from the recorded pressure and volume data. For a crude oil system, the isothermal compressibility coefficient of the oil phase is defined for pressures above the saturation pressure by one of the following expressions (arek, 2006): V C 1 (1) V P 1 C P V can be replaced by relative volume V r. V (2) Vr (3) Vbp Many methodologies exist to derive compressibility from Eq. 1 using CCE data. he measured volume, change in volume, and change in pressure resulting from each CCE step can be entered into the equation. his is the simplest 2 method, but for black oil, for which the change in measured volume can be very small, the measured volume change will be sensitive to measurement error. his measurement error may result in large variability in the obtained compressibility. A method that is less sensitive to noise involves plotting the measured volume versus pressure on a linear scale. A function is then fitted to the data; this can be an exponential function (Eq. 4), a natural logarithmic function (Eq. 5), or other function that fits the measured data. Many fitting functions exist in the industry, but only Eqs. 4 and 5 are discussed here: P c y a b. e (4) y a. ln( P) b (5) he variable y in Eqs. 4 and 5 represents volume when used in combination with Eq. 1, and density when used in combination with Eq. 2. he constants a, b, and c represent fitting parameters. Standard derivative solutions exist for Eqs. 4 and 5. he derivative of volume with respect to pressure from Eq. 4 can be substituted into Eq. 1. When the pressure term is eliminated using Eq. 4, the following expression for compressibility is obtained: V V a C 1 (6) V P V c. Combining Eq. 1 with the derivative of volume with respect to pressure from Eq. 5 yields 1 V 1 a C. (7) V P V P Using Eq. 6 or Eq. 7, isothermal compressibility can be determined for each recorded pressure and volume value. Which fitting function is used depends on the fluid encountered, preference of the laboratory performing the measurements, and the accuracy of the obtained function fit. COMPRESSIBILIY FROM DFA Formation tester DFA measurements that are available today include accurate fluid density and pressure. When Eq. 2 is used instead of Eq. 1, we

3 can derive compressibility from pressure-density data. Using the downhole pump and a formation interface module (Schlumberger, 2006; Dong et al., 2008), fluid is pumped through the tool and into the borehole. A downhole fluid density sensor provides the real-time in-situ density and pressure. Fluid contamination is measured using a dedicated contamination monitoring system. When contamination is below a certain threshold, a fixed volume of fluid in the tool is exposed to either increasing or decreasing pressure. his can be achieved through different methods, discussed in a subsequent section of this paper. he methodology to extract compressibility from density-pressure data is similar to extracting compressibility from volume-pressure data. Measured density, the change in density, and the change in measured pressure can be entered into Eq. 2. Alternatively, an exponential function (Eq. 4), a natural logarithmic function (Eq. 5), or other function can be fitted to the density pressure data. Following similar mathematical manipulations used earlier, Eqs. 8 and 9 can be derived for compressibility: 1 a C (8) P c. 1 1 a C. (9) P P Using Eq. 8 or Eq. 9, isothermal compressibility can be determined for each recorded density and pressure value. IN-SIU DENSIY MEASUREMEN he fluid density sensor is a rod sensor that measures the thermophysical properties of the fluid by the vibration of a mechanical resonator submersed in the flowline fluid and which provide density and viscosity measurements (O Keefe, Erikson, et al., 2007; O Keefe, Godefroy, et al. 2007). o make a measurement, the rod is excited by an electromechanical actuator. he interaction of this excitation with the fluid creates the resonance. he geometrical arrangement is well designed, which can minimize the temperature and pressure effects. From the resonance, the two parameters analyzed are frequency and damping. he frequency will relate to the density and the 3 damping to the viscosity. he basic structure of the density and viscosity sensor is shown in Fig 1. he sensor has integrated electronics, simplifying the characterization and deployment. he sensor measures fluid under flowing or static condition with a zero dead volume providing an accurate measurement. Fig.1 Density and viscosity sensor sketch. he density sensor is designed so the dual resonance modes operate to directly compute density from the resonator-fluid interaction at a 1-s frequency (Fig.2). he key benefit of the dual resonances is to reduce the common mode effects. hose effects include the Young modulus changing with temperature and pressure, instabilities and drifts in the mechanical resonator, electronics frequency stability at high temperature, etc. Using characterization parameters based on the response of standard fluids also enables assessing measurement quality in real time to ensure that the sensor response spectrum is within specification. Fig.2 Density rod dual resonance mode design, displacement mode (left) and sharing mode (right). able 1 shows the specification of the density sensor. he measurement range covers the wide

4 range from 0.05 to 1.2 g/cm 3, with an accuracy of g/cm 3. he pressure rating is 25,000 psi for the extra high pressure version of the sensor. he sensor is able to measure the fluid density at the extra high pressure condition which can fill the gap in fluid laboratory PV testing, which is normally capped by the pressure limit of 15,000 to 20,000 psi capacity. When the reservoir pressure is high with low GOR oil, the density change with pressure change will be small. However, the resolution of the density measurement is as high as g/cm 3, which provides enough measurement variation to estimate the in-situ fluid compressibility. able 1. Density Sensor Specifications Measurement Density Sensor Range, g/cm to 1.2 Accuracy, g/cm 3 ±0.012 Resolution, g/cm 3 ±0.001 Mechanical Conventional Version emperature rating, C 150 Pressure rating, psi 15,000 HPH Version emperature rating, C 175 Pressure rating, psi 25,000 Extra H Version emperature rating, C 190 Pressure rating, psi 25,000 DISCUSSION ABOU ACCURACY A detailed discussion about the accuracy of compressibility is beyond the scope of this paper. However, since a new method of obtaining compressibility downhole is introduced, a comparison with existing methods is warranted. Comparing Eq. 1 with Eq. 2 shows a strong similarity in fundamentals. We therefore simplify the comparison of accuracy to a comparison of the input measurements and methods. Both the downhole and CCE methods require pressure, a fitting parameter, and either volume or density as inputs. Pressure gauges used in both the laboratory and DFA measurements are highly accurate and contribute the least amount to the compressibility 4 error. he pressure gauge used in the case studies has a typical accuracy of 10-4 of the full scale reading and maximum error of of full scale reading. In the case studies for this paper, a 25,000-psi gauge was used which translates to a 2.5 psi and 6.25 psi typical and maximum error, respectively. All case studies were over 6,000 psi pressure, which translates to 0.1% error when pressure is used in the compressibility calculation. When a pressure change is used in the calculation instead of absolute pressure, the error is typically reduced even further. he accuracy of pressure gauges used in CCE experiments might deviate slightly from the DFA gauges, but the impact of the accuracy difference on obtained compressibility results will be insignificant. Relative volume is used in the laboratory computation, and density is used in the downhole method. Density accuracy and resolution is defined in able 2. For oil, which could be argued to range between 0.5 and 0.9 g/cm 3, the error introduced through the density measurement would not exceed 2.5%. his error will increase for gas. When a total pressure change of several thousand psi is exerted onto a black oil, a density change of 0.02 g/cm 3 or larger will typically be recorded. With a sensor resolution of g/cm 3, this represents a 5% error over the recorded range. his error will decrease with increasing compressibility. he accuracy of CCE relative volume is not widely quoted in open literature. In a typical CCE experiment, the PV cell is charged with 30 to 40 cm 3 of fluid. he change in volume resulting from a pressure change is then determined from the cell piston position. In black oil examples examined in this paper, the observed typical single-phase volume change was less than 2 cm 3. From the variability in the recorded volume readings, the error in volume change appears to be approximately 1%. here will be a function-fitting error. he same function-fitting techniques can and must be used on DFA data and CCE experiment data when comparing the two. A wide range of functionfitting quality quantification techniques and quality control techniques are available in the industry. When the quality of the input data is good, then fitting errors are minimal. Indeed, data quality is the key to obtaining reliable

5 compressibility from DFA. As with any measurement, data quality control and processing is required. DFA measurements are made at in-situ conditions on fluid that has not been exposed to surface conditions or bottle transfers. However, the pressure cycles normally contain less stabilization time, and temperature cannot be actively controlled. When formation testing operations are planned, executed, and quality controlled appropriately, the compressibility obtained with the new DFA method can have an accuracy approaching the laboratory CCE method, obtained at in-situ conditions in real time. BES PRACICES AND DFA PLACEMEN SCENARIOS Formation testers are modular, and sensor placement within the toolstring is very flexible (Weinheber 2008) Pressure and density sensors can be run upstream or downstream of the pump (Mullins, 2008; Schlumberger, 2006). he location chosen depends on the specific objectives of the formation testing job. his sensor placement determines the method by which a fluid pressure change can be induced and whether the sensor is exposed to flowing pressure or mud column pressure. Advantages and disadvantages of each scenario will be discussed. Note that only the relevant sensors and generic modules are mentioned. he following configuration, referred to here as setup 1, is very commonly encountered: formation interface, pump, density and pressure sensor, sample receptacle, exit. Fluid is drawn from formation through the formation interface into the pump. Fluid is subsequently expelled from the pump at mud column pressure and passes through the sensors into the borehole. When sample capture is desired, the sampling receptacle is opened and the exit is closed. Fluid is now directed into the sample receptacle. When the sample receptacle is filled, the pump pressurizes the sample and the sample receptacle is closed. Compressibility can be derived from the recorded pressure density data. Additional pressure changes can be induced at any time by closing the exit, even when a sample receptacle is not filled. he fluid in the flowline will be pressurized and the pressure density data recorded. he following points are worth highlighting: 5 Receptacle filling duration (minutes) is short enough to assume constant ambient temperature and long enough to ignore pressure-induced temperature fluctuations. Sample receptacle fluid is exposed to a 4,000 to 10,000 psi pressure increase. Pressure and density DFA data are recorded. Compressibility is calculated from the DFA data. CCE experiments and DFA data are obtained from the same fluid sample. DFA and CCE derived compressibility can be compared. he next configuration will be referred to as setup 2: formation interface, density and pressure sensor, pump, exit. Sample receptacles and additional DFA modules can be placed anywhere in this configuration, but are they are not required to obtain compressibility. Fluid is drawn into the tool through the formation interface and flows past the density and pressure sensors. wo different methods can be used to create a pressure change. Method 1 is typically applied during the late time clean up. he drawdown, defined as the difference between formation pressure and the flowing pressure, is manipulated by changing the pumping rate. As long as the flowing fluid is in single phase and at constant contamination, the density value obtained at different flowing pressures can be plotted, and compressibility can be derived. he following conditions apply to this method: he pressure range obtained may be limited if permeability is high. emperature and contamination must be monitored and constant. Pressure ranges that can be applied are limited by mobility and rate. ypically, the density and pressure data cover formation pressure and may cover saturation pressure. here must be constant contamination for the interval used in this method. Focused sampling can help to clean up the flowline to reach an undetectable contamination level in relatively short time. Method 2 can be called trapped fluid analysis, and it differs slightly from method 1, but may improve the compressibility results significantly. Fluid is again pumped from formation, but when a compressibility measurement is desired, a valve at

6 degf g/cm3 cp unitless ft3/bbl psi SPWLA 55 th Annual Logging Symposium, May 18-22, 2014 the formation inlet is closed. Fluid in the flowline between the closed inlet and the pump is depressurized and a rate determined by the pump. Pressure versus density data is recorded over a large range of pressure, and compressibility is calculated.. With this method Depressurization can be applied step-by-step or continuously Depressurization is short enough to assume constant ambient temperature and long enough to ignore pressure-induced temperature fluctuations. he pressure range that can be applied will be larger and will include saturation pressure. o obtain sufficient data to perform the compressibility estimation, there are other general practices to improve the data quality for all scenarios: Reliable density measurement is critical. Sanding of the formation must be avoided to eliminate any effect on the density measurement and the flowline fluid must be kept single phase. he isothermal condition should be met, so it is necessary to have a temperature sensor to check the measurement interval. With longer cleanup, the temperature normally can achieve stabilization in the flowline. A larger pressure interval will yield more accurate compressibility estimation. Having flexible control over pressurizing or depressurizing trapped fluid in flowline gives the ability to increase the pressure coverage. And for each pressure step, a clear stabilized reading can improve the quality of the data. DENSIY AND COMPRESSIBILIY EXRAPOLAION pressure down to reservoir pressure. his method can be highly accurate, particularly when the difference between mud column pressure and formation pressure is small (e.g., less than 1,000 psi) and the range of pressure over which pressure density data was recorded is large. Additionally, the compressibility value can be extrapolated from compressibility-pressure plots. CASES here are six cases of compressibility estimation in Gulf of Mexico and other deep water fields in the following section, covering different toolstring setups, various pressure ranges, black oil, condensate gas, and water. Case 1: Black Oil. his case is from the Gulf of Mexico, with one fluid sampling station with approximately 3 hours of cleanup using a non-focused probe. Setup 2 is used, indicating the fluid analyzer is located upstream from the pump module. Fluid properties, measured in real-time, include GOR, composition, viscosity, density, pressure, and temperature (Fig.3) IFA_1 GOR_IFA1 Low Quality Medium Quality High Quality CHCR_IFA1[4] CHCR_IFA1[0] CHCR_IFA1[1] CHCR_IFA1[2] CHCR_IFA1[3] RODVIS_IFA1 When a density sensor is placed downstream of a formation tester pump (setup 1), the density data is recorded at or above mud column pressure. However, for reservoir studies (Vinay, 2012) and pressure gradient validation, fluid density at formation pressure is required. A pressure correction can be applied through equation of state modeling based on DFA composition. However, a more accurate result can be obtained by fitting a function to the DFA pressure- density data and extrapolating this function from mud column RODRHO_IFA RODEMP_IFA1 SOIPRES_IFA EIM (min) Fig.3 Fluid scanning and sampling, case

7 cm3/s degf g/cm3 cp unitless ft3/bbl psi SPWLA 55 th Annual Logging Symposium, May 18-22, 2014 he contamination for the samples taken is confirmed by the PV laboratory to be 2.4%. Method 1, which covers the flow period, is applied in this case. o eliminate contamination effects, only the later part of the data with constant contamination is used. able 2 Density and compressibility at reservoir condition Density g/cm 3 Compressibility psi -1 Rod sensor E-06 PV laboratory E-06 Case 2: Black Oil. Fig.4 Density-pressure function fitting. he temperature in the flowline is observed to be constant during the test ensuring the isothermal condition is met. As shown on Fig. 4, the points are carefully selected to cover the maximum pressure interval, increasing the reliability of the approach. In Fig.4, the density and pressure curve is fitted by the modified exponential function, which is shown on the plot. hen the compressibility is estimated based on the fitted parameters. his example, from the Gulf of Mexico, illustrates black oil fluid scanning and sampling. As shown in Fig.6, black oil is pumping at a GOR of approximately 1200 ft 3 /bbl. he sampling was done using a non-focused sampling tool with toolstring setup 2. Contamination of the fluid sample is 2.5%. Fluid cleanup has stabilized towards the end of the station, which can be seen from the GOR/ compositions/ viscosity/ density in Fig IFA_1 GOR_IFA1 Low Quality Medium Quality High Quality CHCR_IFA1[4] CHCR_IFA1[0] CHCR_IFA1[1] CHCR_IFA1[2] CHCR_IFA1[3] 2.5 RODVIS_IFA RODRHO_IFA RODEMP_IFA1 SOIPRES_IFA1 Fig.5 PV density and compressibility comparison he PV density is again shown together with rod density in POFR Fig.5 (left). here is a g/cm 3 difference between rod density and laboratory (PV) density curve. On the right in Fig.5, compressibility results from the laboratory and the rod sensor show good agreement. he results are summarized in able EIM (min) Fig.6 Fluid scanning and sampling, case 2. 7

8 able 3 Density, compressibility at reservoir condition Density g/cm 3 Compressibility psi -1 Rod sensor E-6 PV laboratory E-6 Case 3: Black Oil. Fig.7 Density-pressure fitting function. he temperature in the flowline is unchanged after 70 min, which satisfies the isothermal condition. Method 1, which covers the flow period, is applied in this case. he density and pressure crossplot which covers the data between 76 and 86 min is shown in Fig.7. his case, from the Gulf of Mexico, uses tool string setup 2 at a pressure environment in the approximate range of 22,000 to 25,000 psi. Fig.9 shows the fluid scanning and sampling log. he focused sampling technique was used successfully. Shortly after the flow was split at 56 min, the sample line fluid was clean, which was later confirmed by the PV laboratory results. he selected interval includes the very short period of the final cleanup of the flowline and bottle filling period where the density sensor pressure is increasing to the formation pressure after finishing filling the bottle. While the pressure was increasing, the density was increasing accordingly. Exponential fitting with Eq. 4 is applied on the rod density, and the fitting function is shown on Fig.7. Fig.8 PV density and compressibility comparison. he measured density and calculated compressibility is shown on Fig.8 together with the laboratory CCE results. here is only a g/cm 3 difference in the density measurements, and the compressibility also has a good match. he results are summarized in able.3. 8 Fig.9 Fluid scanning and sampling, case 3. After 80 min, the flowline temperature is stabilized at 209 F, which indicates the isothermal condition is met for the rest of the testing interval.

9 cm3/s degf g/cm3 unitless ft3/bbl psi SPWLA 55 th Annual Logging Symposium, May 18-22, 2014 Method 1, which covers the flow period, is applied in this case. he density-pressure points are carefully selected at each pressure step in the interval of interest and are shown on the left in Fig.10. he exponential function fit to the pressure-density curve can be used to calculate the compressibility at each pressure, which is shown on the right. estimated based on the density function, which is shown in Fig IFA_1 GOR_IFA1 Low Quality Medium Quality High Quality CHCR_IFA1[4] CHCR_IFA1[0] CHCR_IFA1[1] CHCR_IFA1[2] CHCR_IFA1[3] Fig. 10 Compressibility estimation from density sensor Case 4: Condensate Gas. his case shows the compressibility measurement using method 2 with trapped fluid in the flowline. After sample capture has been completed, there is clean fluid still inside the flowline. By closing the formation interface seal valve, this fluid is isolated and can be pressurized or depressurized as needed. At the same time, the pressure, temperature, fluid density, viscosity, GOR, compositions, fluorescence, and other parameters can be measured with the change of the pressure, as shown in Figure 11 for this case. he temperature is observed to be constant during the whole testing period, meeting the isothermal condition. A step by step reduction in flowline pressure is induced by the pumpout module. Dew precipitation is detected at approximately 12 min by the fluid analyzer through the presence of liquid hydrocarbon and dropping of GOR. Only single phase gas must be used for the compressibility analysis. In this case only the pressure-density data obtained at pressures higher than the pressure of the phase change will be used. For each step change of the flowline pressure, the stabilized density values are selected and shown together with corresponding pressure in Fig.12. he density shows the expected curvature; the fitting with Eq. 4 matches the rod density, which is shown in the plot. he compressibility curve is RODRHO_IFA1 RODEMP_IFA1 POFR SOIPRES_IFA EIM (min) Fig.11 Fluid scanning and sampling, case 4. Fig.12 Fluid density of condensate gas case his trapped volume decompression method covers a large pressure range, in this case more than 4,000 psi. his improves the reliability of the compressibility estimation, especially for this high-compressibility fluid. 9

10 ohm.m unitless degf g/cm3 psi SPWLA 55 th Annual Logging Symposium, May 18-22, 2014 pressure of more than 5,000 psi provides a large pressure change that is used apply for the compressibility estimation. he temperature is stable at approximately 164 F and with less than 1 F fluctuation, thus constituting an the isothermal process. Fig. 13 Compressibility for condensate gas case Case 5: Water. his case is a water sampling case with toolstring setup 1, in which the rod density sensor is located downstream of the pumpout module. his shows the benefit of the toolstring setup that can overpressure the sample bottle thousands of psi above hydrostatic pressure; the large pressure interval makes it possible to have good estimation of compressibility, even if the fluid is nearly incompressible. RODRHO_IFA1 IFA_1 Fig.15 Water density measurement for four bottles. For all the four samples, as shown on Figure 15, the density measurement indicates good repeatability and overall shows a clear trend RODEMP_IFA1 SOIPRES_IFA1 LEGS_IFA1 WAF_IFA1 HAFF_IFA1 FFRES_IFA Fig.16 Water pressure-density curves for four consecutive bottles EIM (min) Fig.14 Fluid scanning and sampling for water. Water contamination is monitored by the resistivity cell in the fluid analyzer, and Fig. 14 shows good cleanup, achieving clean fluid. As shown in the figure, after 150 min, four bottles are filled. After filling the bottles, the pumpout module continues to pressurize the bottles, results in the steep pressure and density increases observed in the top two tracks. his increase in 10 Fig.17 Water compressibility from different bottles.

11 degf g/cm3 unitless ft3/bbl psi SPWLA 55 th Annual Logging Symposium, May 18-22, 2014 All each bottle, the compressibility can be estimated separately from the pressure-density curves plotted in Figure 16. he compressibility curves estimated from different bottles (shown on Fig.17) overlay, showing the consistency of the results GOR_IFA1 CHCR_IFA1[4] CHCR_IFA1[0] CHCR_IFA1[1] CHCR_IFA1[2] CHCR_IFA1[3] RODRHO_IFA1 275 RODEMP_IFA1 SOIPRES_IFA EIM (s) Fig.19 Fluid scanning and sampling for light oil. Fig. 18 Density and compressibility extrapolated to reservoir pressure. Since the rod density sensor is located at downstream of the pump, it only measures the properties at and higher than the hydrostatic pressure. With the curve fitting on Fig.16 and Fig.17, the density and compressibility can be extrapolated to reservoir pressure. As shown in Fig.18, the density and compressibility extrapolation results from four bottles have a very good agreement. Case 6: Black Oil. his is a light oil sampling station with toolstring setup 1 in which the rod density sensor is located downstream of the pumpout module. he pump pressurized the bottles 5,000 psi over hydrostatic pressure after filling the bottles, which provides a high quality data with significant pressure-density variation. Fluid cleanup is achieved using focused sampling. After 1.6 hr the flowing fluid is free of contamination. he temperature variation is less than 1 F during the sample over pressuring, which can be treated as isothermal condition. hree consecutive samples are taken at the end of the station. he overpressure for each bottle can be seen as pressure and density spikes on Fig.19. Fig.20 Light oil pressure-density fitting for three bottles. For each bottle, the pressure-density curves are fitted by Eq. 4. he fitting functions are showing on Fig.20. All the curves have a good fit. he compressibility can be estimated by Eq. 8 based on the density curve fitting for each bottle. he compressibility estimation curves for the three bottles in Fig.20 show good agreement for all the bottles. Fig.21 Compressibility from three bottles. 11

12 Since the density is measured at hydrostatic pressure, fluid density at reservoir pressure is extrapolated. Fig.22 shows the density and compressibility extrapolated to reservoir pressure. he laboratory measured density was g/cc. In this case study, compressibility and pressure corrected densities gave very consistent results. Reservoir Evaluation & Engineering 11 (6): SPE PA. McCain, W.D., 1990, he Properties of Petroleum Fluids: ulsa, Oklahoma, PennWell Publishing. ISBN Mishra, V. K., Skinner, C., MacDonald, D. et al., 2012, Downhole fluid analysis and asphaltene nanoscience coupled with vertical interference testing for risk reduction in black oil production: SPE Annual echnical Conference and Exhibition, paper SPE Mullins, O.C., 2008, he Physics of Reservoir Fluids; Discovery through downhole fluid analysis, Houston, exas, Schlumberger. ISBN-10: Fig.22 Density / compressibility extrapolation. CONCLUSION With in-situ density measurement in the formation testing tool, fluid compressibility can be determined under reservoir conditions, downhole in real time. his is a new approach that is based on in-situ fluid density instead of using the relative volume method. he approach is applicable for the reservoir fluids from nearly incompressible fluid to highly compressible fluid. he accuracy of the compressibility measurements from this approach can be similar to that from a laboratory, as confirmed by the case studies. he method provides the real-time answers which enables field study at early stage. REFERENCES Achourov, V., Gisolf, A., Kansy, A., Eriksen, K.O., O'Keefe, M., and Pfeiffer,., 2011, Applications of accurate in-situ fluid analysis in the North Sea: Paper SPE MS presented at Offshore Europe, Aberdeen, UK, 6 8 September. Dong, C., O'Keefe, M.D., Elshahawi, H., Hashem, M., Williams, S., Stensland, D., Hegeman, P, Vasques, R., erabayashi,., Mullins, O.C., and Donzier, E., 2008, New downhole-fluid-analysis tool for improved reservoir characterization: SPE O Keefe M., Eriksen K. O., Williams S., Stensland D., and Vasques R., 2007, Focused sampling of reservoir fluids achieves undetectable levels of contamination: Paper SPE presented at SPE Asia Pacific Oil & Gas Conference, Jakarta, Indonesia, 30 October 1 November. O Keefe, M., Godefroy, S., Vasques, R., Agenes, A., Weinheber, P., Jackson, R., Ardila, M., Wichers, W., Daungkaen, S., De Santo, I., 2007, In-situ density and viscosity measured by wireline formation testers: Paper SPE presented at SPE Asia Pacific Oil & Gas Conference and Exhibition, Jakarta, Indonesia, 30 October 1 November. Schlumberger, 2006, Fundamentals of formation testing: Houston, exas, Schlumberger. arek A., 2006, Reservoir Engineering Handbook hird Edition: Gulf Professional Publishing. ISBN-10: Weinheber P,, Gisolf AG., Jackson RR., De Santo I., 2008, Optimizing Hardware Options for Maximum Flexibility and Improved Success in Wireline Formation esting, Sampling and Downhole Fluid Analysis Operations: Paper SPE presented at Nigeria Annual International Conference and Exhibition held in Abuja, Nigeria, 4-6 August

13 ABOU HE AUHOR Li Chen is a Senior Reservoir Engineer and Associate Reservoir Domain Champion with Schlumberger, Houston, exas. He has received the Master s Degree in Reservoir Engineering from China Petroleum University. His previous positions include senior reservoir engineer, associate reservoir domain champion, answer product analyst for formation testing in China. Adriaan Gisolf is a Reservoir Domain Champion with Schlumberger, based in Sugar Land. Previous positions held in Schlumberger include Field Engineer in Indonesia and Nigeria, Service Quality Coach in Colombia and Reservoir Domain Champion in Angola and Norway. He holds a master's degree in mechanical engineering from Delft University of technology. Beatriz E. Barbosa is a Reservoir Pressure & Sampling Product Champion with Schlumberger, Wireline HQ. Under her responsibilities are the alignment of the domain road map with the industry needs and the development of the required technologies. Her previous positions include Wireline Geomarket manager (Peru, Colombia and Ecuador), Middle East & Asia Wireline raining Center Manager and Country Wireline operations manager, and Field Engineer and echnical Sales representative in Angola, Colombia and Ecuador. Beatriz holds a degree in Bsc. Civil Engineering from Los Andes University in Bogota, Colombia. Dr. Julian Youxiang Zuo is currently a Scientific Advisor and FLCN Interpretation Architect at Schlumberger Houston Pressure & Sampling Center leading the effort to develop new answer products for new formation testing tools. He has been working in the oil and gas industry since 1989 and coauthored more than 160 technical papers in peer-reviewed journals, conferences and workshops. Zuo holds a Ph.D. degree in chemical engineering from the China University of Petroleum in Beijing. Vinay K. Mishra is Principal Reservoir Engineer and Domain Champion with Schlumberger, Houston, X. Previously he has worked in different roles of petroleum engineering based in Canada, Libya, Egypt and India. He has co-authored over 25 publications in international conferences including SPWLA and SPE. He has done B.S. in Petroleum Engineering from Indian School of Mines, Dhanbad, India. Vinay has been committee member and session chairs in several of SPE events. He is also registered with Association of Professional Engineers and Geoscientists of Alberta (APEGA). Hadrien Dumont is a Reservoir Domain Champion with Schlumberger, based in Houston. Previous positions held in Schlumberger include Field Engineer in Norway, Kazakhstan, and Malaysia and Reservoir Domain Champion in Egypt, Sudan, Syria, Indonesia, and the United States. He holds a MSc in Mining Engineering from University Libre de Bruxelles, Belgium and a MSc in Petroleum Engineering from Institut Francais du Petrole, France. homas Pfeiffer is a Reservoir Domain Champion in Schlumberger, Stavanger, Norway. He has received Master s Degree in Petroleum Engineering in exas A&M University, Master s Degree in Electrical Engineering from echnical University of Munich, Germany. His previous positions held in Schlumberger include Field Engineer in North Sea, Egypt, Netherlands, Austria, Gulf of Mexico, and Location Manager in Austria and Hungary. Vladislav Achourov is a Reservoir Domain Champion with Schlumberger, based in Norway. He has received Master's Degree in Physics from Russian University of Oil & Gas. He joint Schlumberger in Russia and worked with reservoir simulations and production engineering. In his current role he provides technical support for wireline formation testing. 13