Overcoming Downhole Fluid Sampling Challenge Using Dual-Port Straddle Packer in Shallow Viscous Reservoir

Objectives/Scope: Downhole fluid sampling of high quality, low contaminated oil samples with a pumpout wireline formation tester (PWFT) in a shallow unconsolidated reservoir with high H2S, high water salinity and filled with viscous oil is a quite challenging operation. Key properties, related to fluid flow in low pressure reservoirs: formation mechanical weakness, drilling invasion and the high contrast on fluid mobility, have resulted in the failure or impracticality of conventional methods for efficient sampling, resulting in a long sampling time causing high rig cost overhead and often highly contaminated oil samples. Most common problems faced during sampling are: Sand production- causing caving and lost seals and no pressure or samples. Sand plugging of the tool flowline. Operation limitation of pressure drawdown- dictated by extremely low formation pressure and mainly due to having saturated pressure around 20 to 30 psia below formation initial pressure (based on 118 bubble point samples measured in the laboratory). To maintain rock stability and low pressure draw down, fluids were pumped at a low rate, resulting in a long operation time, where a single sample take up to 15 – 20 hours of a pump out. Even with the long pumpout time the collected sample is often highly contaminated based on laboratory PVT analysis report. Methods, Procedures, Process Understanding of the formation properties and its rock mechanics helps to design proper operating techniques to overcome the challenge of viscous oil sampling in unconsolidated sand reservoir. A pre-job geomeechanical study of unconfined sand with very low compressive strength, restricted the flow rate to a maximum drawdown per square inch to maintain rock stability while pumping out. Dual-Port Straddle Packer (figure 1) sampling was introduced to overcome the mentioned challenges. Its large flow area (>1000 in² in 8 ½″ OH section) allowed a high total pumping rate while maintaining very low flow rate per square inch at the sand face, which resulted in an ultra-low draw-down flowing pressure to prevent sand collapse and producing below bubble point pressure that could invalidate further PVT studies. Packer inflation pressure has also been limited to a maximum of 150 to 200 psia above hydrostatic pressure to achieve isolation without overcoming the sand weak compressive strength. During the clean-out operation crude oil tend to separate from water based mud (WBM) filtrate in the packed-off interval due to fluid density difference and immiscibility of the two liquids due to the lower shear rate applied (among others). So a water/oil interface forms within the packed-off interval. As pumping continues, this oil/water fluid contact moves toward the bottom inlet port allowing more clean oil to accumulate at the top. Results, Observations, Conclusions: With the advantage of the dual inlet port straddle packer and the independent opening/closing operating design of each port, a clean segregated oil sample was collected from the top port at an early stage of job operation, saving rig time and cost without compromising collected fluids quality that is valid for PVT studies. Novel/Additive Information: Dual-port Straddle Packer with large flow area (plus filters) with ultra-low drawdown pressure to stay above bubble point pressure in shallow heavy oil reservoirs resulted to be another provided a cost effective technology that can be utilized for collecting downhole samples (DHS) that will undergo PVT studies.

Influence of reservoir lithology on porous flow resistance of gas-bearing tight oil reservoirs and production forecast

The release of dissolved gas during the development of gas-bearing tight oil reservoirs has a great influence on the effect of development. In this article, the high-pressure mercury intrusion experiment was carried out in cores from different regions and lithologies of the Ordos Basin and the Sichuan Basin. The objectives are to study the microscopic characteristics of the porous throat structure of these reservoirs and to analyze the porous flow resistance laws of different lithology by conducting a resistance gradient test experiment. A mathematical model is established and the oil production index is corrected according to the experiment results to predict the oil production. The experimental results show that for tight reservoirs in the same area and lithology, the lower the permeability under the same back pressure, the greater the resistance gradient. And for sandstone reservoirs in different areas, the resistance gradients have little difference and the changes in the resistance coefficients are similar. However, limestone under the same conditions supports a much higher resistance gradient than sandstone reservoirs. Furthermore, the experimental results are consistent with the theoretical analysis indicating that the PVT (pressure–volume-temperature) characteristics in the nanoscale pores are different from those measured in the high-temperature, high-pressure sampler. Only when the pressure is less than a certain value of the bubble point pressure, the dissolved gas will begin to separate and generate resistance. This pressure is lower than the bubble point pressure measured in the high-temperature and pressure sampler. The calculation results show that the heterogeneity of limestone reservoirs and the mismatch of fluid storage and flow space will make the resistance, generated by the separation of dissolved gas, have a greater impact on oil production.

A new derivative-based method for determination of bubble point pressure of hydrocarbon systems

The bubble point pressure is essential for planning and managing oil field development and production strategies. The conventional procedure of the determination of bubble point pressure and volume is a trial-and-error method. Consequently, this leads to the lack of uniqueness, accuracy, and repeatability of the solution. This paper describes a new technique that utilizes the pressure–volume (PV) data obtained from the constant-composition expansion (CCE) test to determine the bubble point pressure of hydrocarbon systems. This method is a derivative-based procedure where consecutive derivative ratios form peaks. The highest peak always exists at the inflection of PV data to traverse into a two-phase region. A new mathematical model based on the exponential-power function is introduced to accurately describe the PV data above and below the bubble point. The new model leads to the direct determination of both bubble point pressure and volume simultaneously. Uniqueness, accuracy, and repeatability in the new method are guaranteed regardless of who performs the calculation.

Phase Behavior Analysis of CO2 and Formation Oil System

In this paper, there are nine oil samples to explore the characteristics of formation oil at different CO2 injection rate, and the characteristics include bubble point pressure, volume expansion coefficient, viscosity, density, and average molecular weight, composition of gas and liquid phase, and asphalt sediment. According to the experimental results of early nine oil samples of swelling tests, in high temperature and high pressure conditions, characteristics and changing rules of properties of formation oil, including bubble point pressure, volume expansion coefficient, viscosity, density, and average molecular weight, composition of gas and liquid phase, and asphalt sediment, were evaluated and analyzed at different CO2 injection rate systematically. The research not only can provide guides for petroleum engineers when they need to adjust the injection and production programs, but also can provide comparatively comprehensive experimental rules for researches on enhanced oil recovery (EOR) mechanisms of gas miscible and nonmiscible flooding. Moreover, phase parameters of different formation oil system can be extracted for reservoir numerical simulation.

Thermodynamic Modeling of Mutual Solubilities in Gas-Laden Brines Systems Containing CO2, CH4, N2, O2, H2, H2O, NaCl, CaCl2, and KCl: Application to Degassing in Geothermal Processes

With the growing interest in geothermal energy as a renewable and sustainable energy source, nowadays engineers and researchers are facing technological and environmental challenges during geothermal wells’ operation or energy recovery improvement by optimizing surface installations. One of the major problems encountered is the degassing of geothermal brines which are often loaded with dissolved gases, resulting in technical problems (scale formation, corrosion, reduced process efficiency, etc.) and environmental problems through the possible emission of greenhouse gases (CO2, CH4 and water vapor) into the atmosphere. In this work, a method to predict, from readily available information such as temperature and GLR, the bubble point pressure of geothermal fluids as well as the GHG emission rate depending on the surface conditions is presented. This method is based on an extended version of the Soreide and Whitson model with new parameters optimized on the solubility data of several gases (CO2, CH4, N2, O2 and H2) in brine (NaCl + CaCl2 + KCl). The developed approach has been successfully used for the prediction of water content of different gases and their solubilities in different types of brines over a wide temperature and pressure range, and has been applied for the prediction of bubble point pressure and GHG emissions by comparing the results with available industrial data of geothermal power plants including the Upper Rhine Graben sites.

Developing a Model to Predict Oil Viscosity Using Specific Gravity and Formation Volume Factor as Correlating Parameters

This paper presents the research work on using a machine learning algorithm to predict the viscosity of Niger Delta oil reservoirs using formation volume factor and fluid density at bubble point pressure as correlating parameters. Oil Viscosity stands out when considering the amount of oil recoverable from the reservoir hence it is an important input into the recovery factor computation, material balance analysis, reservoir simulation/history match, EOR evaluations and many other applications. Laboratory techniques of obtaining oil viscosity are quite expensive and time consuming, hence the need for various mathematical correlations developed for its estimation. Majority of the correlations make use of empirical and experimental relationships developed from analyzing oil samples to obtain a trend to predict viscosity mostly for a basin. None of these has been developed for oil viscosity for Niger Delta fluids. Viscosity has been globally defined as the resistance to shear stresses within the fluid or the resistance of the fluid molecules to deformation. For a typical reservoir fluid system, where the liquid and gas exist in dynamic equilibrium, reservoir fluid composition along with temperature and pressure has been established to determine reservoir fluid viscosity1. Hence for an isothermal system and at a defined pressure in the reservoir the viscosity will be dependent on largely the composition. The reservoir fluid composition is also represented by the reservoir fluid density and the formation volume factor; therefore it is possible to deduce the viscosity of reservoir fluids from the oil density and formation volume factor even though a direct relationship has not been established between these parameters. Therefore, a correlation that can establish a relationship between the specific gravity (density) and FVF with viscosity will have significant value in the oil and industry. The data used for this analysis includes viscosity, formation volume factor, oil density at 2800 sample bubble point pressure. The data was obtained by analyzing over 3500 PVT Analysis reports, extracting the data points using a python work program, cleaning up the data and removing erroneous data, performing preliminary analysis to establish baseline relationships between the data. Supervised learning using a classification tree model was used as the machine learning approach. Seven different machine learning algorithms were reviewed, and the Random Forest Regressor was selected as the most suitable algorithm for the prediction. The model prediction results were quiet encouraging as the model was able to predict viscosity within 10% deviation from the experimental viscosity for over 80% of the cases resulting in about 90% prediction accuracy. The analysis of the results further revealed that the model could better predict viscosity of Medium to Light oil with an R2 value of between 0.90-0.96 without adjusting some obvious erroneous data points. Future of this research work will involve further in-depth analysis which will merge the preliminary QC plots with the results to evaluate the effect of the outlier sample points on the final predictability of the model. Also explore other machine learning models to further improve predictability and be able to predict viscosity across other pressure values other than the bubble point pressure to capture viscosity along the producing life of the reservoir.

Development Features of Cold Production with Horizontal Wells in a Foamy Extra-Heavy Oil Reservoir

The block M as a foamy extra-heavy oil field in the Carabobo Area, the eastern Orinoco Belt, has been exploited by foamy oil cold production utilizing horizontal wells. The early producing area of block M has been put into production more than 10 years. And the development features of cold production in foamy extra-heavy oil reservoirs are different from the conventional oil field. It is necessary to investigate the development features of this kind reservoir and analyze its influence factors. Combining the production data with the reservoir geological characteristics of the research area, the cold production features of foamy extra-heavy oil using horizontal wells are analyzed. Then numerical simulations were adopted to study the influence factors of cold production performance. In the early stage of cold production, the oil production rate is high and the producing GOR is low. With the process of cold production, the reservoir pressure decreases gradually, the producing GOR increases gradually, and the oil production rate decreases gradually. When the bottom hole flowing pressure drops to below the bubble point pressure, the flow of extra-heavy oil in the reservoir can be divided into two zones: far well zone and near well area. In the far well zone, the pressure is higher than the bubble point pressure. The flow of oil is a single-phase flow, and the displacement mode is elastic driving. In the near well area, the pressure is lower than the bubble point pressure, and the oil flow is foamy oil flow, and the displacement mode is the dissolving gas drive driven by foamy oil. There exists many factors that influence the cold production performance of foamy extra-heavy oil, including reservoir depth, reservoir thickness, reservoir physical property and heterogeneity. The oil recovery factor per unit pressure drop can evaluate the cold production performance of foamy extra-heavy oil reservoirs. The effectiveness of cold production is closely related to reservoir parameters. Larger reservoir thickness, deeper reservoir depth and greater reservoir permeability will enhance the performance of cold production. Closer, larger and more interlayers above the horizontal well will hinder the performance of cold production. This research provides certain guidance and reference for further development adjustment and new project evaluation for foamy extra-heavy oil reservoirs in the Eastern Orinoco Belt.