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.
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 (
In this paper, we combine a fast wave equation solver using boundary integral methods with a global optimization method, namely Particle Swarm Optimization (PSO), to estimate an initial velocity model. Unlike finite difference methods that discretize the model space into pixels or voxels, our forward solver achieves significant computational savings by constraining the model space to a layered model with perturbations. The speed and reduced model space of the forward solve allows us to use global optimization methods that typically require numerous evaluations and few unknown variables. Our technique does not require an initial guess of a velocity model and is robust to local minima, unlike gradient descent frequently used in methods for both initial velocity model estimation and full waveform inversion. We apply our inversion algorithm to several synthetic data sets and demonstrate how prior information can be used to greatly improve the inversion.
Most seismic processing techniques rely on an accurate velocity model to obtain meaningful results. Incorrect velocity models can hamper processing and lead to erroneous interpretation of seismic data. In particular, inversion techniques such as Full Waveform Inversion (FWI) are highly sensitive to the initial velocity. Without a good initial velocity estimate, FWI will converge to local minima with artifacts (Virieux and Operto, 2009).
Unlike global seismology, exploration and regional scale velocity models can be poorly constrained. Initial velocity models can be built using a variety of techniques such as travel time tomography, NMO semblance analysis, and even full waveform inversion at very low frequencies (Woodward et al., 2008). Most methods of constructing an initial velocity model rely on expensive travel-time or wave equation solvers and use gradient based approaches that are susceptible to local minima. Furthermore, travel time tomography does not use the full wavefield and requires the picking of arrivals as well as long offsets. Frequently noise levels are too high for full waveform inversion to be performed at frequencies where the problem is sufficiently convex and thus it can be plagued by local minima Pratt (1999); Sirgue (2006).
To combat the problem of local minima in geophysical inverse problems, researchers have applied global optimization techniques that are less susceptible to local minima (Sambridge and Mosegaard, 2002; Sen and Stoffa, 2013). Researchers also make use of different objective functions and regularization techniques (Burstedde and Ghattas, 2009; van Leeuwen and Herrmann, 2013) to make the inversion more convex, mitigating the need for global solvers.
In this paper we integrate a fast Helmholtz solver with the global optimization method Particle Swarm Optimization (PSO) to invert a velocity model without an initial model. We first give an overview of the field expansion method we use for quickly solving the Helmholtz equation and modify it to reduce artifacts and accurately simulate exploration scale data. We then describe two variations of the PSO algorithm for inverting a velocity model from an observed data set. Finally, we present the results of our inversion algorithm on synthetic data and demonstrate how prior information about the velocity model can be used to improve the inversion.
Li, Q. P. (State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, The Chinese Academy of Sciences) | Chen, B. R. (State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, The Chinese Academy of Sciences)
The micro-seismic monitoring work during linear excavation process of deep tunnel has the following characteristics: many working faces are constructed simultaneously; the sensor arrays are located in the rear of the working face; the vertical direction coordinate gap of all sensors is small. Due to the above characteristics, traditional Geiger’s location method cannot work out stable solution. With the micro-seismic monitoring work of Jinping II hydropower station, this article forms Newton second order method and Newton downhill method to improve former method. The rock-burst location results with three methods are proposed and their calculation abilities are compared. The results show that computational accuracy of the second method is influenced by initial value and the third method has the global convergence; At last, the last two methods together are adopted for source location of five rock-bursts. The conclusion indicates that that improves the accuracy and stability of algorithm convergence.
With deep exploitation of mineral resources and development of underground space, there are more and more underground engineering with increasing rock-burst accidents caused by high geostress . Therefore effective monitoring and prediction for rock-burst is one of the most important approaches to guarantee the safety of deep geotechnical engineering and micro-seismic monitoring technology plays a pivotal role in it for it can effectively monitor the position of rock fracture  and begin to be used in some fields like mine safety monitoring and hydropower underground engineering.
3D position of micro-seismic source is an important parameter in the monitoring research, and how to locate the seismic source accuracy and efficiency has always been an important content. Most of the location methods are extended from earthquake location, such as classic Geiger method, relative positioning method, double residual method, Bayesian method  . These methods have greatly promoted the progress of the study on micro-seismic source location.
In the field of mine micro-seismic monitoring, Chen  applies particle swarm algorithm to improve the positioning accuracy; Lin  utilizes linear positioning algorithm to determine the iterative initial value and employs Geiger method for accurate position, which receives the desired effect; Dong  combines epicenter coordinates with wave velocity as unknown, which effectively avoids the influence of inaccurate velocity. The studies above is all belong to mine micro-seismic monitoring field, however, little research is known on source location during linear tunnel excavation process.