The petro-elastic model (PEM) represents an integral component in the closed-loop calibration of integrated four-dimensional (4D) solutions incorporating time-lapse seismic, elastic and petrophysical rock property modeling, and reservoir simulation. Calibration of the reservoir simulation model is needed so that it is not only consistent with production history but also with the contemporaneous subsurface description as characterized by time-lapse seismic. The PEM requires dry rock properties in its description, which are typically derived from mechanical rock tests. In the absence of those mechanical tests, a small data challenge is posed, whereby all necessary data is not available but the value of reconciling seismic attributes to simulated production remains. A seismic inversion-constrained n-dimensional metaheuristic optimization technique is employed directly on three-dimensional (3D) geocellular arrays to determine elastic and density properties for the PEM embedded in the commercial reservoir simulator.
Ill-posed dry elastic and density property models are considered in a field case where the seismic inversion and petrophysical property model constrained by seismic inversion exist. An n-dimensional design optimization technique is implemented to determine the optimal solution of a multidimensional pseudo-objective function comprised of multidimensional design variables. This study investigates the execution of a modified particle swarm optimization (PSO) method combined with an exterior penalty function (EPF) with varied constraints. The proposed technique involves using n-dimensional design optimization to solve the pseudo-objective function comprised of the PSO and EPF given limited availability of constraints. In this work, an examination of heavily and reduced-order penalized metaheuristic optimization processes, where the design variables and optimal solution are derived from 3D arrays, is conducted so that constraint applicability is quantified. While the process is examined specifically for PEM, it can be applied to other data-limited modeling techniques.
Integration of time-lapse seismic data into dynamic reservoir model is an efficient process in calibrating reservoir parameters update. The choice of the metric which will measure the misfit between observed data and simulated model has a considerable effect on the history matching process, and then on the optimal ensemble model acquired. History matching using 4D seismic and production data simultaneously is still a challenge due to the nature of the two different type of data (time-series and maps or volumes based).
Conventionally, the formulation used for the misfit is least square, which is widely used for production data matching. Distance measurement based objective functions designed for 4D image comparison have been explored in recent years and has been proven to be reliable. This study explores history matching process by introducing a merged objective function, between the production and the 4D seismic data. The proposed approach in this paper is to make comparable this two type of data (well and seismic) in a unique objective function, which will be optimised, avoiding by then the question of weights. An adaptive evolutionary optimisation algorithm has been used for the history matching loop. Local and global reservoir parameters are perturbed in this process, which include porosity, permeability, net-to-gross, and fault transmissibility.
This production and seismic history matching has been applied on a UKCS field, it shows that a acceptalbe production data matching is achieved while honouring saturation information obtained from 4D seismic surveys.
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.
Azevedo, Leonardo (Cerena/Decivil, Instituto Superior Técnico) | Demyanov, Vasily (Institute of Petroleum Engineering, Heriot-Watt University) | Lopes, Diogo (Cerena/Decivil, Instituto Superior Técnico) | Soares, Amílcar (Cerena/Decivil, Instituto Superior Técnico) | Guerreiro, Luis (Partex Oil & Gas)
Geostatistical seismic inversion uses stochastic sequential simulation and co-simulation as the perturbation techniques to generate and perturb elastic models. These inversion methods allow retrieve high-resolution inverse models and assess the spatial uncertainty of the inverted properties. However, they assume a given number of a priori parametrization often considered known and certain, which is exactly reproduce in the final inverted models. This is the case of the top and base of main seismic units to which regional variogram models and histrograms are assigned. Nevertheless, the amount of existing well-log data (i.e., direct measurements) of the property to be inverted if often not enough to model variograms and its histograms are biased towards the more sand-prone facies. This work shows a consistent stochastic framework that allows to quantify uncertainties on these parameters which are associated with large-scale geological features. We couple stochastic adaptive sampling (i.e., particle swarm optimization) with global stochastic inversion to infer three-dimensional acoustic impedance from existing seismic reflection data. Key uncertain geological parameters are first identified, and reliable a priori distributions inferred from geological knowledge are assigned to each parameter. The type and shape of each distribution reflects the level of knowledge about this parameter. Then, particle swarm optimization is integrated as part of an iterative geostatistical seismic inversion methodology and these parameters are optimized along with the spatial distribution of acoustic impedance. At the end of the iterative procedure, we retrieve the best-fit inverse model of acoustic impedance along with the most probable value for the location of top and base of each seismic unit, the most likely histogram and variogram model per zone. We couple stochastic adaptive sampling (i.e., particle swarm optimization) with global stochastic inversion to infer three-dimensional acoustic impedance from existing seismic reflection data. Key uncertain geological parameters are first identified, and reliable a priori distributions of potential values are assigned to each parameter. The type and shape of each distribution reflects the level of knowledge about this parameter. Then, particle swarm optimization is integrated as part of an iterative geostatistical seismic inversion methodology and these parameters are optimized along with the spatial distribution of acoustic impedance. At the end of the iterative procedure we retrieve the best-fit inverse model of acoustic impedance along with the most probable value for the location of top and base of each seismic unit, the most likely histogram and variogram model per zone.
Fang, Qian (Beijing Jiaotong University) | Li, Ao (Beijing Jiaotong University) | Zhong, Yue (Beijing Jiaotong University) | Liu, Yan (Beijing Jiaotong University) | Zhang, Dingli (Beijing Jiaotong University)
Blasting operation is one of the most efficient techniques for rock breakage in tunnel engineering. Meanwhile blasting energy is dissipated through the ground which may produce some negative effects, such as ground vibration, flyrock, explosive noise, and air blast pressure. Ground vibration is one of the most undesirable effects induced by blasting operation in mountain tunnels which could cause negative impacts on the residents living nearby and adjacent structures. The correlation between the structural vibration and the vibration velocity of particles is more closely related than that between the displacement and the acceleration. Therefore the ground vibration effects can be well represented by peak particle velocity (PPV) on the ground.
In this research, we use the microseismic monitoring technique to observe the PPV of the mountain surface, below which the Badaling Great Wall Railway Station is constructed. The maximum excavation span of the station reaches 32.7m and is excavated below the Great Wall. A total of 53 sets of monitoring results of the station caused by blasting inside the station are collected. The monitoring results include the PPV, the blasting charge, the distance from blasting point to ground surface, and the moment magnitude. The effects of the blasting energy, the rockmass condition, and the geological topography on the PPV are studied. Regression analysis are also conducted to relate PPV to associated parameters. The obtained relationship can be used to predict the responses of the Great Wall due to blast inside the station. Moreover, we can use the research results to determine the proper blasting charge of station excavation.
When a mountain tunnel is excavated using the drilling and blasting method, the vibration due to blast inevitably produces negative impacts on the surface structures (Ak et al., 2009; Nateghi, 2012; Verma et al., 2018). The appropriate evaluation of the blasting vibration is of fundamental importance in safeguarding the existing structures adjacent to tunnelling. The peak particle velocity (PPV) is the key parameter, commonly adopted to identify the blasting vibration impacts on existing structures (Hasanipanah et al., 2017).Peak particle velocity refers to the maximum speed of a particular particle as it oscillates about a point of equilibrium that is moved by a passing wave, which is proportional to the produced energy and dynamic stress due to blasting (Faradonbeh et al., 2016). The PPV is one of the best single descriptor for correlating case history data with vibration-induced damage (New, 1986; Sharif, 2000). Both the analytical solutions (Sambuelli, 2009; Arora and Dey, 2010) and empirical solutions (Jiang and Zhou, 2012; Xia et al., 2018) have been proposed to calculate the PPV produced by blast. Numerical simulations (Saiang and Nordlund, 2009; Verma et al., 2018) have also been used to obtain the PPV associated with blast. In addition, the artificial intelligence methods, including artificial neural networks, genetic algorithms, and fuzzy expert systems have been conducted to predict the PPV value (Dehghani and Ataee-Pour, 2011; Monjezi et al., 2011; Amnieh et al., 2012; Faradonbeh et al., 2016).
The work represents the results of design and development of object-relational geospatial database which combines the diversity of geological and geophysical data and enables to represent the data as an object of linear algebra for further analysis by means of machine learning algorithms.
Li, Lei (Central South University) | Xie, Yujiang (University of Hamburg) | Gajewski, Dirk (University of Hamburg) | Tan, Yuyang (University of Science and Technology of China) | Tan, Jingqiang (Central South University)
Fast and accurate source location is crucial for microseismic monitoring. Stochastic optimization algorithm is derivative-free and just need random solutions as the initial model, and it is quite suitable for non-linear seismic location problem. In this work, we utilize differential evolution, which is a fast and robust global optimization method and belongs to evolutionary algorithms, to speed up microseismic location with waveform-based methods. Parameter tuning of differential evolution for two waveform-based location methods, namely diffraction stacking and cross correlation stacking, is studied and reference ranges of individual parameters are obtained. Field data examples indicate that parameter tuning is necessary to ensure the performance of differential evolution, and the convergence features of the imaging functions of different stacking operators for microseismic source location can be revealed.
Presentation Date: Wednesday, October 17, 2018
Start Time: 9:20:00 AM
Location: Poster Station 15
Presentation Type: Poster
We present a framework that enables estimation of low-dimensional reservoir properties directly from seismic data, without requiring the solution of a high dimensional seismic inverse problem. Our workflow is based on the Bayesian evidential analysis approach and exploits learning the direct relation between seismic data and reservoir properties to efficiently estimate reservoir properties, as well as generate samples from the posterior distribution. We discuss methods of learning highly informative summary statistics from seismic data, which help minimizing computational costs of the approach. We demonstrate the efficacy of our approach by estimating the posterior distribution of reservoir net-to-gross for sub-resolution thin-sand synthetic reservoir.
Presentation Date: Tuesday, October 16, 2018
Start Time: 8:30:00 AM
Location: 209A (Anaheim Convention Center)
Presentation Type: Oral
Othman, Abdullah (King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia) | Mesbah, Wessam (King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia) | Iqbal, Naveed (King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia) | Al-Dharrab, Suhail (King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia) | Muqaibel, Ali (King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia) | Stuber, Gordon (Georgia Institute of Technology)
In this paper we consider the problem of maximizing the information theoretic sum-rate in a wireless geo-seismic acquisition system. Successive interference cancellation decoding is assumed at the gateway nodes, and Shannon capacity bounds are used to search for the optimal set of geophones to be decoded at each gateway. These optimization algorithms are simulated and compared, where it is shown that the ant system achieves the highest sum-rate compared to other algorithms.
Presentation Date: Tuesday, October 16, 2018
Start Time: 9:20:00 AM
Location: Poster Station 11
Presentation Type: Poster
We carry out the inversion of marine controlled-source electromagnetic data using real coded genetic algorithm to estimate the isotropic resistivity. Unlike linearized inversion methods, genetic algorithms belonging to class of stochastic methods are not limited by the requirement of the good starting models. The objective function to be optimized contains data misfit and model roughness. The regularization weight is used as a temperature like annealing parameter. This inversion is cast into a Bayesian framework where the prior distribution of the model parameters is combined with the physics of the forward problem to estimate the aposteriori probability density function in the model space. The probability distribution derived with this approach can be used to quantify the uncertainty in the estimation of vertical resistivity profile. We apply our inversion scheme on three synthetic data sets generated from horizontally stratified earth models. For all cases, our inversion estimated the resistivity to a reasonable accuracy. The results obtained from this inversion can serve as starting models for linearized/higher dimensional inversion.
Presentation Date: Monday, October 15, 2018
Start Time: 1:50:00 PM
Location: Poster Station 13
Presentation Type: Poster