We interpreted a series of single-well-chemical-tracer-tests (SWCTTs) estimating residual oil (SORW) to base high salinity waterflood, low salinity waterflood and subsequent polymer flood conducted on a Greater Burgan well. Interpretation of the tests requires history matching of the back-production of partitioning and non-partitioning tracers which is impacted by differing amounts of irreversible flow and differing amounts of dispersion as well as the amount of residual oil.
We applied the state-of-the-art chemical reservoir simulator (UTCHEM) and an assisted history matching tool (BP’s Top-Down-Reservoir-Modeling) to interpret the tests and accurately quantify uncertainty in residual oil saturations post high salinity, low salinity, and polymer floods. Two optimization algorithms (i.e., Genetic algorithm (GA) and Particle-Swarm-Optimization (PSO)-Mesh-Adaptive-Direct-Search (MADS) algorithms) were applied to better address the uncertainty.
Our results show a six saturation unit decrease in SORW post low salinity with no change to the SORW post polymer. This is in-line with our expectations - we expect no change in SORW post-polymer as the conventional HPAM, which does not exhibit visco-elastic behavior, was used in the test. We demonstrate that history matching the back-produced tracer profiles is a robust approach to estimate the SORW by showing that three-or four-layer simulation model assumption does not change the SORW estimated. We accounted for the uncertainty in partition-coefficient in our uncertainty estimates.
We present several innovations that improve history matching back-produced tracer profiles; hence, better SORW estimations (e.g., different level of dispersivity for individual simulation layers to account for different heterogeneity level as opposed to assuming a single dispersion for all layers). We generate more robust estimates of uncertainty by finding a range of alternative history matches all of which are consistent with the measured data.
Time-lapse seismic monitoring is a powerful technique for reservoir management and the optimization of hydrocarbon recovery. In time-lapse seismic datasets, the difference in seismic properties across different vintages enables the detection of spatio-temporal changes in saturated properties and structure induced by production. The main objectives are (1) to identify bypass pay zones in time-lapse seismic data for the deepwater Amberjack field, located in the Gulf of Mexico, (2) confirm the identified bypass pay zones in the results of reservoir simulation, and (3) recommend well planning strategies to exploit these bypassed resources.
A high-fidelity seismic-to-simulation 4D workflow that incorporates seismic, petrophysics, petrophysical property modeling, and reservoir simulation was employed, which leveraged cross-discipline interaction, interpretation, and integration to extend asset management capabilities. The workflow addresses geology (well log interpretation and framework development), geophysics (seismic interpretation, velocity modeling, and seismic inversion), and petrophysical property modeling (earth models and co-located co-simulation of petrophysical properties with P-impedance from seismic inversion). An embedded petro-elastic model (PEM) in the reservoir simulator is then used to affiliate spatial dry rock properties with saturation properties to compute dynamic elastic properties, which can be related to multi-vintage P-impedance from time-lapse seismic inversion. In the absence of the requisite dry rock properties for the PEM, a small data engine is used to determine these absent properties using metaheuristic optimization techniques. Specifically, two particle swarm optimization (PSO) applications, including an exterior penalty function (EPF), are modified resulting in the development of nested and average methods, respectively. These methods simultaneously calculate the missing rock parameters (dry rock bulk modulus, shear modulus, and density) necessary for dynamic, embedded P-impedance calculation in the history-constrained reservoir simulation results. Afterward, a graphic-enabled method was devised to appropriately classify the threshold to discriminate non-reservoir (including bypassed pay) and reservoir from the P-impedance difference. Its results are compared to unsupervised learning (k-means clustering and hierarchical clustering). From seismic data, one can identify bypassed pay locations, which are confirmed from reservoir simulation after conducting a seismic-driven history match. Finally, infill wells are planned, and then modeled in the reservoir simulator.
Summary After Fan introduced the concept of synthetic aperture (SA) to marine controlled-source electromagnetics (CSEM), numerous of optimization methods are applied to SA weights selection for improving the detectability of deeply buried targets under seabed, but few are suitable for the seriously nonlinear EM problems. This study presents an application of particle swarm optimization (PSO) to optimization of the phase shift and the amplitude compensation coefficient of SA for marine CSEM. Eigenstate analysis (EA), another nonlinear SA weights optimization method, is also carried out in marine CSEM data processing for comparison. The 3D synthetic model reconstructed from Fan (2010) is used to better demonstrate the effects of the detectability with and without two optimization algorithms. In order to validate the effectiveness of PSO, we scan all the confined weights.
Owing to increasing fuel price and strict environmental legislation, the use of hybrid electric propulsion systems (HEPSs) has been increasing in recent years. Optimal design has been introduced to play a greater role of HEPS in fuel saving, emission reduction and economic improving. However, the optimization of fuel consumption may lead to degeneration in greenhouse gas (GHG) emission and net present value (NPV). A multi-objective optimization for the HEPS, taking account of fuel consumption, GHG emission and NPV, is discussed in this paper. GHG emission model and NPV model are established to make sure that the solution is environmental friendly and economically feasible. The effectiveness of the proposed method is demonstrated by a case of an anchor handling tug supply vessel (AHT). The HEPS designed by proposed method improves all of three objectives for more than 10% compared with the conventional propulsion system. In addition, the proposed method outperform the optimization of fuel consumption in GHG emission and NPV at the expense of slight reduction in fuel saving. Furthermore, the NPV sensitivity analysis suggests that the development trends in fuel price, electric price and battery industry will help the HEPS to make more economic sense in the future.
Oil price recovering and more rigorous environment legislations have motivated the development of hybrid electric propulsion system (HEPS) (Geertsma, Negenborn, Visser and Hopman, 2017). As shown in Fig. 1(a), diesel engines in HEPS are coupled with generators and power the propellers without mechanical connection, allowing constant-speed-operation under dynamic loads. Redundant energy from generator is stored in the energy storage system (ESS), which acts as a secondary energy source and enables peak shaving. Therefore, HEPS is born with the potential for fuel saving, greenhouse gas (GHG) emission reduction and profit promotion. In recent years, several ships have already been built or retrofitted with HEPS, most of which are offshore supply vessels and tugboats (Ovrum and Bergh, 2015).
It is worth noting that, the advantages of HEPS can not be fully exerted without comprehensive optimization considering fuel consumption, GHG emission and net present value (NPV), which has not been reported. Skinner, Parks and Palmer (2009) employed Genetic algorithm (GA) in the optimal design of submarine propulsion system targeting on maximum overall efficiency of the propulsion system. The comparison of three drive topologies suggested that the design with hybrid electric/mechanical drive architecture is the most efficient. Soleymani, Yoosofi and Kandi-D (2015) optimally designed a HEPS for a medium-size boat taking fuel consumption as objective. A fuzzy-thermostat strategy was introduced to manage the energy flow between the main components, which were optimal sized by particle swarm optimization (PSO) algorithm. Simulation results indicated that the HEPS leads to a 40 % reduction in fuel consumption comparing to the conventional propulsion system. However, in those researches, GHG emission and NPV were not considered which results in sub-optimal design. On one hand, the implementation of HEPS aims to improve the propulsion efficiency (fuel consumption) but also cut the GHG emission. On the other hand, NPV is an indicator of the profitability of the system taking cost and income into consideration, which are crucial for the possibility of widespread application of HEPS. Actually, the minimization of fuel reduction may result in an oversized ESS and the overuse of shore power, which are environmentally unfriendly and economical infeasible.
An accurate estimation of viscosity values is imperative for an optimal production and transport design of hydrocarbon fluids. Based on this requirement, precise and robust empirical correlation models are highly requested. While there are numerous correlation models from literature, most models are inadequate to predict an accurate oil viscosity using unbiased data. This study aims to develop new and improved empirical viscosity correlations through available field measurements on the NCS. The performance of the proposed models is then studied through a comparative analysis with published correlations from literature.
New correlation models are developed for dead, gas saturated and undersaturated oils using Particle Swarm Optimization (PSO) and Radial Basis Function Network (RBFN). The first technique is a computational optimization algorithm that aims to improve a function with respect to a specified objective function, while the latter is an artificial neural network model that utilizes different radial basis functions as activation functions.
The optimization algorithm was used to re-calculate the coefficients of established viscosity correlation expressions while maintaining their functional form. The results show that the modified correlation models are more in agreement with the test data for all three oil types using the defined parameters from literature, compared to the established empirical correlations and the RBFN. The new correlations provide a mean absolute percentage error of 15.08% and 17.41% and 3.35%, for dead, saturated and undersaturated oil viscosity, respectively. The highly accurate result in the latter correlation is linked to the input variables, as the undersaturated viscosity is a function of saturated viscosity, which is presumed known.
The results of this study make it reasonable to conclude that the proposed correlation methods are more in-line with the measured viscosity on the NCS, compared to the discussed correlation models from literature.
We use the mixed-integer nonlinear optimization algorithm called Particle Swarm Optimization and Mesh Adaptive Direct Search to optimize the design of seismic surveys. Due to the conflicting goals of obtaining a good subsurface illumination at the lowest possible cost, we apply a bi-objective optimization strategy that searches the best options in the illumination and cost senses while builds a Pareto front that shows the trade-off between illumination and cost and allows the survey designer to choose the specific amount of each one of them. The Particle Swarm Optimization part is used to escape local minima and the mixed-integer part is used to deal with integer aspects of a seismic survey design like the number of receivers and sources, to name but a few.
Presentation Date: Monday, September 25, 2017
Start Time: 3:55 PM
Location: Exhibit Hall C/D
Presentation Type: POSTER
Olalekan, Fayemi (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Beijing 100029, China) | Di, Qingyun (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Beijing 100029, China)
This study introduces the application of an improved implementation work flow for centered-centered progressive PSO (IRCCPSO) inversion technique for Multi-transient electromagnetic method (MTEM) full waveform inversion. The stabilizing functional was used to introduce the constraint in the inversion algorithm; thus, the global best position was updated using multi-objective functional. Firstly, 1D study using conventional IRCCPSO technique was presented. Furthermore, 2D inversion study over a buried resistive body model was carried out using a limited search space. The obtained inversion results were good representation of the earth model. Consequently, this confirms the effectiveness of the IRCCPSO technique as a good geophysical tool for MTEM full waveform inversion.
Presentation Date: Wednesday, September 27, 2017
Start Time: 2:40 PM
Location: Exhibit Hall C, E-P Station 2
Presentation Type: EPOSTER
In this paper we propose a proxy model based seismic history matching (SHM), and apply it to time-lapse (4D) seismic data from a Norwegian Sea field. A stable proxy model is developed for generating 4D seismic attributes by using only the original baseline seismic data and dynamic pressure and saturation predictions from reservoir flow simulation. This method (
In this study we firstly perform a check on the validity and accuracy of the proxy approach following the methodology of (
Seismic traveltime tomography is an optimization problem that requires large computational efforts. Therefore, linearized techniques are commonly used for their low computational cost. These local optimization methods are likely to get trapped in a local minimum as they critically depend on the initial model. On the other hand, common global optimization techniques such as Genetic Algorithm (GA) or Simulated Annealing (SA) are insensitive to the initial model but are computationally expensive and require many controlling parameters. Particle Swarm Optimization (PSO) is a rather new global optimization approach with few parameters that has shown excellent convergence rates and is straightforwardly parallelizable, allowing a good distribution of the workload. However, while it can traverse several local minima of the evaluated misfit function, classical implementation of PSO can get trapped in local minima at later iterations as particles inertia dim.
We propose a Competitive PSO (CPSO) to allow "worst" particles to explore the model parameter space and eventually find a better minimum. A tomography algorithm based on CPSO is successfully applied on a 3D synthetic case corresponding to a typical calibration shot geometry in a hydraulic fracturing context.
Presentation Date: Tuesday, October 18, 2016
Start Time: 1:25:00 PM
Location: Lobby D/C
Presentation Type: POSTER
Gao, Rujiang (Wuhan University of Technology) | Xu, Yanmin (Hubei Key Laboratory of Inland Shipping Technology) | Wang, Dangli (Wuhan University of Technology) | Zou, Chunming (Hubei Key Laboratory of Inland Shipping Technology) | Jin, Cheng (Wuhan University of Technology)
With the implementation of the Strategies along River Economic Support Strip, bridges cluster construction has been formed in many waters such as the Yangtze River, Pearl River and other waterways. Ship-bridge collision accidents caused by multibridges influences occurred frequently. Therefore, it is quite seriously necessary and urgent to carry out the ship-bridge collision avoidance relevant studies. For the problems of ship route planning in multi-bridges water area, after analyzing the features of multi-bridges water area and the sailing regulation constraints, the unconstrained and multi-constrained route cost function models were established. By bringing several dynamic obstacles in a static environment model, the dynamic obstacles environment model in the multi-bridges water area was established. According to introducing dynamic collision detection modules method and putting forward several optimal objectives including the shortest route distance, safety navigating without collisions, Particle Swarm Optimization with Outer Penalty Function was applied to plan a Multi-Constraints ship dynamic route without collisions under 3 types of traffic flows environment with multiple dynamic obstacles in the multi-bridges water area. By using Matlab simulation platform, the effectiveness of the algorithm was verified to ensure the safety of ships navigation and the multibridges. The conclusions could be used for intelligent autopilot of ships, and for synergy search and rescue of Unmanned Surface Vehicles (USV).
With the implementation of the Strategies along River Economic Support Strip, Bridges Cluster Construction has been formed in many waters such as the Yangtze River, Pearl River and other waterways. Resulted in ships’ navigation difficulty surging and ship-bridge collision accidents occurred frequently. Therefore, it is quite seriously necessary and urgent to carry out the ship-bridge collision avoidance relevant studies. There are many types of research methods for ship’s route planning, focused on unmanned aerial vehicles (USV), Unmanned Surface Vehicles (USV), missiles and other route planning research fields (Chen Hua,2015. Liu Fan,2015. Altmann Arne,2013. Lee, Joon- Woo,2012).However, it involves less researches for ship’s route planning. By analyzing the characteristics of multi-bridges water area and sailing regulations constraints, Multi-Constraints route cost function model was established. By introducing a number of dynamic obstacles in a static environmental model, dynamic obstacles environment model in the multi-bridges water area was built. According to putting forward dynamic collision detection modules method and applying Particle Swarm Optimization with Outer Penalty Function (PSO-OPF), a Multi-Constraints ship dynamic route without collisions was planned under 3 types of different traffic flows environment with multiple dynamic obstacles in the multi-bridges water area. The results of planning could lay a foundation for further ship’s route planning research in the multi-bridges water area.