Paraffin or wax deposition under two-phase gas-oil slug flow in horizontal pipes was studied experimentally. The experiments were conducted using a 2.067-in large-scale multiphase flow loop under an operating pressure of 350 psig. Testing fluids were Garden Banks condensate and natural gas. Hydrodynamic experiments were performed prior to the wax deposition experiments to verify the flow patterns and examine the flow behavior. The hydrodynamic and heat-transfer variables were estimated using the unified mechanistic model (Zhang et al., 2003). Wax deposition experiments were conducted under single-phase and slug flows with parametric variation of oil and gas superficial velocities and testing durations of 4, 12, and 24 hrs. The bulk fluid and initial pipe wall temperatures at a removable spool piece were kept below WAT and relatively constant to control the initial concentration driving force. In this study, unprecedented detailed measurement and analysis of the circumferential local samples were conducted. A new pigging tool was designed and constructed for selective wax sampling at top, side, and bottom quarters of the circumference of the removable spool piece. Local deposit thicknesses were calculated from the direct measurements of deposit mass and density. Wax samples were analyzed by using DSC and HTGC for wax content and composition. The impact of the physics of the slug flow on wax deposition was investigated. The results indicated that deposit thickness, wax content, and wax mass were affected by the change in superficial velocities or the flow rates of the phases. It was found that the deposit thickness increased with time. The thickness decreased with increasing superficial liquid velocity; whereas, it increased with increasing superficial gas velocity. The trends of the thickness were found to crossover each other at a certain time for different superficial gas velocities. In slug flow, circumferential variations of the deposit characteristics were analyzed. The deposit at the top wall was thicker, softer, and contained more oil than the bottom. Wax fraction increased with time reflecting the aging. Moreover, wax fraction increased with higher superficial liquid and gas velocities at a given time. The crossover of the wax fraction trends with different superficial gas velocities occurred. In slug flow, deposit at the top wall always had lower wax fraction than the bottom. Average carbon number and WAT of the deposit relatively increased with wax fraction. Wax deposits under slug flow had longer chain paraffins compared to the single-phase flow with the same wax fraction. Wax mass at the top wall was higher than the bottom. This new set of experimental data can be used as a verification tool for further development of wax deposition mechanistic model under such conditions.

Summary Paraffin or wax deposition under two-phase gas/oil slug flow in horizontal pipes is studied experimentally. The experiments are conducted by use of a 2.067-in., large-scale multiphase flow loop under an operating pressure of 350 psig. Testing fluids are Garden Banks condensate and natural gas. Two-phase-flow hydrodynamics and single-phase paraffin-deposition experiments are conducted and used as base cases for a comprehensive data analysis. Deposition experiments are conducted with parametric variation of oil and gas superficial velocities and testing durations of 4, 12, and 24 hours. The bulk fluid and initial pipe-wall temperatures at a removable spool piece are kept below wax-appearance temperature (WAT) and relatively constant to ensure consistent initial dissolved-wax-concentration differences and to avoid thermal restriction in the growth of the deposit. Unprecedented detailed measurement and analysis of the circumferential local samples are conducted by use of a newly designed pigging tool with high-temperature-gas-chromatography (HTGC) and differential-scanning-calorimetry (DSC) deposit-characterization techniques. The results reveal that deposit thickness decreases with the increase in the superficial liquid velocity (vSL) for all two-phase-flow cases. The crossover trends of the thickness vs. time are suggested by the single-phase deposition data to occur at times less than 6 hours. Circumferential analysis shows that, for the slug-flow wax deposit, the deposit at the top wall is thicker (by a factor of approximately 1.9), softer, and contains more oil than the bottom deposit. The final overall deposit wax contents are found to increase with the superficial gas velocity (vSG) for the case of vSL = 1 ft/sec, but nonmonotonic change occurs for the case of vSL = 3 ft/sec. Wax deposits under slug flow have longer chain n-alkane compared with single-phase flow having the same wax-fraction cases. Wax mass at the top wall is greater (by a factor of approximately 1.7) than the bottom mass. This new set of complete experimental data serves as the fundamental understanding of single-phase and two-phase gas/oil slug-flow paraffin deposition.

Summary In simulating enhanced-oil-recovery (EOR) processes, it is critical that all the flow behaviors be properly accounted for in the simulation. Because of computation limitations, long calculation time, and complexity of physics, geological models cannot be directly used for fieldwide simulations. Upgridding reduces the number of gridblocks in the simulation model and therefore makes the simulation more efficient. An appropriate upgridding process needs to preserve the dynamic behavior of the fine-scale model. We propose such an analytical methodology. Our new technique is based on preserving the characteristics, which are based on the fractional-flow concept specifically modified for vertical flow between the layers. We develop our method with a specific application to gravity-dominated displacement. In upgridding the fine-scale model, we have developed a criterion by which the sequence in which the fine-scale layers are combined is proposed such that fractional-flow characteristics based on the fine-scale model are honored. Using this methodology, we can determine not only the sequence in which layers are combined, but also to what extent we can upgrid the fine-scale model. The proposed methodology is developed for two-phase, 2D flow under the effect of gravity-segregated displacement. However, it is also tested for three-phase, 3D flow in gravity-dominated displacement with moderate effect of viscous and capillary forces. The proposed solution is analytical; therefore, it is computationally efficient. We have validated the methodology with both synthetic and field examples and demonstrate that the proposed methodology is superior to conventional proportional layering and variance-based methodologies.

Optimal well placement remains both highly challenging and significantly important in the E&P business since they impact field development decision making. Conventionally, well placement is performed manually based on well spacing, which may not capture the effect of reservoir geology effectively, especially in cases of high reservoir heterogeneities. Modern techniques tackle this problem by treating well locations as discrete optimisation problems through reservoir simulations, and thus apply heuristic algorithms to search for optimal well locations. However, these methods require considerable computational effort, which forestall any efforts at novel techniques in searching to for global optimal solutions. This paper presents an innovative well placement optimisation workflow to minimize the calculation time of simulation using drainage volume via streamlines time-of-flight. A reservoir simulation is run for a short period of time to acquire streamlines for all proposed well locations. The time-of-flight property, along streamlines, indicates the theoretical time required for a theoretical tracer particle to move along each streamline to a producer (pressure sink). The time-of-flight, together with reservoir properties, are then used to calculate the hydrocarbon drainage volume from each producer. In which, it is the key parameter to suggest that how much hydrocarbon can move to wells with a given production period. This workflow will search for optimal well locations to maximize the hydrocarbon drainage volume with a given number of wells. The approach translates reservoir simulation to numerical matrix union optimisation, which can be carried out at an extremely fast computational speed (less than a second for a single iteration). The expedited calculation efficiency allows exhaustive search algorithms to evaluate millions of possible well combinations and can, consequently, guarantee a global optimal solution. The workflow has been conceptually proven with a synthetic 2D simulation model, providing a pattern-like scheme to mimic the conventional approach. Furthermore, it has been successfully tested with field scale reservoir simulations. The algorithm demonstrates the advantages of optimized well-placement over conventional methods without much of an increased computational burden. The workflow is also designed to be automated with a simple user-interaction via MATLAB and MS-Excel; namely, the SMARTDRAIN package. This allows engineers/geologists to implement it as a generic workflow without requiring extensive knowledge in mathematical algorithms. With such calculation efficiency and improved optimal solution, this approach can be applied as a new well placement optimisation standard that would add competitive value in field development planning and optimisation.

Beam pump and ESP are common artificial lift techniques in pumping systems. They are widely used as primary oil recovery methods, but system failures lead to production deferments and increases in operating expenses. Employing decades of our field data, promising data science techniques are discussed here to analyze the factors governing failures in both beam pump and ESP approaches. These data are then applied with machine learning models to predict service life, failure mechanism performance, and production deferments. The data analytics process begins with data preparation. Field data were extracted, transformed and loaded into a data warehouse for further processing. These data were categorized by failure information, pump configuration, wellbore geometry, and production information. The significance of each parameter causing pump failures was derived using a process called "Attribute Forward Selection (AFS)." Then several machine learning algorithms were implemented and compared with to determine the most appropriate model to predict pump service life. More suitable pump configurations to improve pump service life were conceptually recommended based on the analysis. Differences in parameter significance was identified by attribute forward selection, and is displayed in a heat map. It was seen that the use of beam pumps in highly tortuous wells received the number one ranking as the main cause of failures whereas sand production was revealed as the most significant parameter relating to ESP failures. Correlations for these parameters were mapped by machine learning algorithms, resulting in multivariate failure prediction models (i.e. involving more than one parameter at a time) to predict the service life of beam pump and ESP systems. For both artificial lift systems, the models with the best correlation found thus far are based on a neural network, which resulted in the highest R-squared values when compared to other techniques. This neural network model was validated with the actual information, and the outcomes using this model are presented via a scatter plot in this paper. The plot shows that the prediction for ESP forms a trend around the theoretical best match line. In contrast, the prediction for Beam pump still needs improvement, with the data being scattered around the straight line with a unity slope. Data science is an emerging technology that recently has provided breakthrough results for big data analysis. This paper will demonstrate the application of such discipline to the area of artificial lift. Machine learning is a promising tool which could help improve human understanding of complex problems, and, in this case, could furnish a durable competitive advantage to the oil and gas industry.