A Single Artificial Neural Network Model Predicts Bubble Point Physical Properties of Crude Oils

Reservoir fluid properties at bubble points play a vital role in reservoir and production engineering computations. Ideally, the bubble point physical properties of crude oils are obtained experimentally. On some occasions, these properties are neither available nor reliable; then, empirically derived correlations or artificial neural network models are used to predict the properties. This study presents a new single multi-input multi-output artificial neural network model for predicting the six bubble point physical properties of crude oils, namely, oil pressure, oil formation volume factor, isobaric thermal expansion of oil, isothermal compressibility of oil, oil density, and oil viscosity. A large database comprising conventional PVT laboratory reports was collected from major producing reservoirs in the Middle East. The model input is constrained mathematically to be consistent with the limiting values of the physical properties. The new model is represented in mathematical format to be easily used as empirical correlations. The new neural network model is compared with popular fluid property correlations. The results show that the developed model outperforms the fluid property correlations in terms of the average absolute percent relative error.

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.

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.

Cyclic Production Scheme for High WC and Low-Pressure Wells for Reservoir Management

Lower Burgan (LB) is one of the most mature reservoirs in north Kuwait divided in to 3 layers, first layer under partial depletion drive and suffer from low reservoir pressure, the second layer under edge-partial water drive and the last layer under active water drive. Increasing trend of water production and high GOR has become a big challenge to control the reservoir production, several of studies done to evaluate the best strategy to enhance the reservoir performance. A comprehensive review of the performance of the wells was conducted to diagnose the specific reason and necessary remedial measures to be adopted. The review included the assessment of the results of the material balance and numerical simulation studies, depletion strategies, wettability/ relative permeability footprints; proximity to the OWC; past completion practices, well integrity and the time lapse PNC/PLT/ Well testing data. Workflows were developed for the sublayers within the hydraulic unit for systematic water cut diagnostics and preventive steps. Identification of suitable technology to address water cut and GOR management was also done. The typical trend of water cut and GOR performance with time and the depletion strategy was established to add value to the ongoing production activities and well allowable for each hydraulic unit. Rate sensitive performance was analyzed for the integration into the production plans. Cyclic production is identified as one of the new ways to reduce the water production and maintain the production for the wells producing below the bubble point. The concept was tried at high water cut wells successfully to revive the well from about 100% to 87% water cut with a closure cycle for 3 month and sustaining the production for high GOR wells with good ESP performance. In addition, suitable candidates have been identified for Coning Control Completions to weaken the water encroachment into the downhole wellbore. The overall water cut for the reservoir has been stabilized during last one year, thus helping the water handling constraints at the gathering facility. This paper will discuss the successful approach to control coning and water encroachment for active Bottom and Edge-Water drive layer and how this approached helped to sustain the production on high GOR wells running below bubble point with necessary diagnostics and remedial measures.

Artificial Intelligence-Based, Automated Rapid Reservoir Assurance and Reservoir Health Diagnostics in a Complex Offshore Mature Field

The QQ Field in the Gulf of Suez is a mature, geologically complex with multiple stacked, faulted reservoirs, with commingled production between different reservoirs. This paper illustrates the power of an automated tool to perform systematic, rapid, and detailed assessment of the reservoir performance, identify the key recovery obstacles and prepare remedial plans to enable the reservoir to produce to its full potential. The well and reservoir data were processed to compute a series of metrics and key performance indicators at various levels (well, layer, reservoir, well groups, etc.). The tool has several automated modules to facilitate rapid, metric-driven reservoir assurance and management. These modules include: (i) well production/injection allocation, (ii) wells decline curve analysis including event-detection, (iii) pressure and voidage analysis, and (iv) Contact analysis. Using performance analytics, the study quickly identified ways to improve the health of the reservoir and maximize its value. The QQ Field predominantly produces from two formations: Nubia and Nezzazat. Furthermore, there are multiple sub-layers in each formation. Reliable flow unit allocation is critical to gauge contribution of each layer, identify the undrained areas of the reservoir, and locate future development opportunities. The flow unit allocation module incorporates all available data such as PLT/ILT data, completion history, permeability of each flow unit at well level, relative pressures, and water influx model. Based on the allocated production, the current recovery factors in Nubia and Nezzazat are approximately 60% and 20% respectively. Analysis of RFT data reveals good vertical communication across Nubia. However, in some areas there is clear pressure discontinuity across layers. The reservoir pressure has dropped below the bubble point in both formations. As a result, water injection was initiated. The pressure in all parts of Nubia was restored above bubble point. Aquifer influx is sufficient to support the current withdrawal rates and further water injection is unnecessary. However, Nezzazat has a significantly higher reservoir complexity and therefore, shows a large variation in pressure behavior. It needs water injection to maintain the reservoir pressure above the bubble point in all parts of the reservoir. Based on the flow-unit allocation, the voidage replacement ratio (VRR) was calculated for each area and each layer. Even though the overall VRR in the waterflooded areas is above one, the distribution of the injected water is uneven. Redistributing injected water and ensuring that all the areas and all the layers are adequately supported will help to maximize recovery. The prolonged dip in oil price demands extreme efficiency. Sound reservoir management must not require unreasonable time or manpower. The rapid, automated analysis enables quick identification of the key areas for improvement in reservoir management practices and maximize the value of the asset.

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.