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.
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
Chemical EOR is one of the promising methods to improve the oil recovery. However, due to high cost of the process, there are challenges to minimize the cost and maximize the oil recovery. Some influencing parameters should be taken into account in a systematic approach to find their impact on oil recovery and accordingly optimizing the process.
In this study, we present a robust optimization workflow of alkaline-surfactant (AS) flooding into a thin clastic reservoir of a field in the Malay Basin. There are coreflood experiments and pilot tests on this field that can be quite helpful to provide a basis to find out the appropriate range of input parameters. Optimization work is based on response surface methodology (RSM) and particle swarm optimization (PSO) technique that aid us to indicate the optimum oil recovery from chemical flooding. In order to get the utmost advantage of this workflow, the waterflooding should be optimized prior to the chemical flooding optimization to maximize the sweep efficiency and oil recovery from the chemical flood.
Evaluation of coreflood and pilot tests indicated that some parameters need supplementary evaluation to investigate their effect on reservoir performance and flow dynamics. These parameters include residual oil reduction by chemical, relative permeability curves, chemicals adsorption, chemical concentration, slug size, injection rate, and initiation time of chemical injection. Based on the result of tornado chart, residual oil reduction and injection rate exhibited highest and lowest impact on oil recovery. RSM was used to explore the relationship between input variables and objective function. Some design parameters such as chemical concentration, slug size and initiation time were examined in this stage. Afterwards, proxy models have been built using polynomial regression and neural network methods. The results showed that the proxy model by neural network method revealed better performance for prediction of the simulation results. The proxy model was used to calculate the oil recovery for any combination of input parameters. Besides, it was used to assess the parameter sensitivity and identify the impact of any input parameter on oil recovery. At the next stage, PSO method was utilized to optimize the oil recovery by chemical flooding. It was found that the optimized water injection rate and pattern for water flooding scenario need further optimization to improve the sweep efficiency and thereby oil recovery by AS flooding at later stage. Running numerous simulation cases is normally expected to optimize the process by conventional methods and the proposed PSO approach can be used to reduce the number of runs significantly. Sensitivity analysis provided a very good understanding about reserve ranges for the different influential parameters. Optimizing the cost of chemical flooding and improving oil recovery are other outcomes of this study.
A novel particle swarm optimization (PSO) method for discrete parameters and its hybridized algorithm with multi-point geostatistics are presented. This stochastic algorithm is designed for complex geological models, which often require discrete facies modeling before simulating continuous reservoir properties. In this paper, we first develop a new PSO method for discrete parameters (Pro-DPSO) where particles move in the probability mass function (pmf) space instead of the parameter space. Then Pro-DPSO is hybridized with the single normal equation simulation algorithm (SNESIM), one of the popular multipoint geostatistics algorithms, to ensure the prior geological features. This hybridized algorithm (Pro-DPSO-SNESIM) is evaluated on a synthetic example of seismic inversion, and compared with a Markov chain Monte Carlo (McMC) method. The results show that the new algorithm generates multiple optimized models with the convergence rate much faster than the McMC method.