To enhance the applicability of localization to various history-matching problems, the authors adopt an adaptive localization scheme that exploits the correlations between model variables and observations. The aim of this work is to present the effectiveness of a fully integrated approach for ensemble-based history matching on a complex real-field application.
A challenging problem of automated history-matching work flows is ensuring that, after applying updates to previous models, the resulting history-matched models remain consistent geologically. To enhance the applicability of localization to various history-matching problems, the authors adopt an adaptive localization scheme that exploits the correlations between model variables and observations. This paper presents a novel approach to generate approximate conditional realizations using the distributed Gauss-Newton (DGN) method together with a multiple local Gaussian approximation technique. This work presents a systematic and rigorous approach of reservoir decomposition combined with the ensemble Kalman smoother to overcome the complexity and computational burden associated with history matching field-scale reservoirs in the Middle East. This paper presents a comparison of existing work flows and introduces a practically driven approach, referred to as “drill and learn,” using elements and concepts from existing work flows to quantify the value of learning (VOL).
Calibrating production and economic forecasts (objective functions) to observed data is a key component in oil and gas reservoir management. Traditional model-based data assimilation (history matching) entails first calibrating models to the data and then using the calibrated models for probabilistic forecast, which is often ill-posed and time-consuming. In this study, we present an efficient regression-based approach that directly predicts the objectives conditioned to observed data without model calibration.
In the proposed workflow, a set of samples is drawn from the prior distribution of the uncertainty parameter space, and simulations are performed on these samples. The simulated data and values of the objective functions are then assembled into a database, and a functional relationship between the perturbed simulated data (simulated data plus error) and the objective function is established through nonlinear regression methods such as nonlinear partial least square (NPLS) with automatic parameter selection. The prediction from this regression model provides estimates for the mean of the posterior distribution. The posterior variance is estimated by a localization technique.
The proposed methodology is applied to a data assimilation problem on a field-scale reservoir model. The posterior distributions from our approach are validated with reference solution from rejection sampling and then compared with a recently proposed method called ensemble variance analysis (EVA). It is shown that EVA, which is based on a linear-Gaussian assumption, is equivalent to simulation regression with linear regression function. It is also shown that the use of NPLS regression and localization in our proposed workflow eliminates the numerical artifact from the linear-Gaussian assumption and provides substantially better prediction results when strong nonlinearity exists. Systematic sensitivity studies have shown that the improvement is most dramatic when the number of training samples is large and the data errors are small.
The proposed nonlinear simulation-regression procedure naturally incorporates data error and enables the estimation of the posterior variance of objective quantities through an intuitive localization approach. The method provides an efficient alternative to traditional two-step approach (probabilistic history matching and then forecast) and offers improved performance over other existing methods. In addition, the sensitivity studies related to the number of training runs and measurement errors provide insights into the necessity of introducing nonlinear treatments in estimating the posterior distribution of various objective quantities.
An ensemble-based history-matching framework is proposed to enhance the characterization of petroleum reservoirs through the assimilation of crosswell electromagnetic (EM) data. As one of advanced technologies in reservoir surveillance, crosswell EM tomography can provide a cross-sectional conductivity map and hence saturation profile at an interwell scale by exploiting the sharp contrast in conductivity between hydrocarbons and saline water. Incorporating this new information into reservoir simulation in combination with other available observations is therefore expected to enhance the forecasting capability of reservoir models and to lead to better quantification of uncertainty.
The proposed approach applies ensemble-based data-assimilation methods to build a robust and flexible framework under which various sources of available measurements can be readily integrated. Because the assimilation of crosswell EM data can be implemented in different ways (e.g., components of EM fields or inverted conductivity), a comparative study is conducted. The first approach integrates crosswell EM data in its original form which entails establishing a forward model simulating observed EM responses. In this work, the forward model is based on Archie's law that provides a link between fluid properties and formation conductivity, and Maxwell’s equations that describe how EM fields behave given the spatial distribution of conductivity. Alternatively, formation conductivity can be used for history matching, which is obtained from the original EM data through inversion using an adjoint gradient-based optimization method. Because the inverted conductivity is usually of high dimension and very noisy, an image-oriented distance parameterization utilizing fluid front information is applied aiming to assimilate the conductivity field efficiently and robustly. Numerical experiments for different test cases with increasing complexity are carried out to examine the performance of the proposed integration schemes and potential of crosswell EM data for improving the estimation of relevant model parameters. The results demonstrate the efficiency of the developed history-matching workflow and added value of crosswell EM data in enhancing the characterization of reservoir models and reliability of model forecasts.
In this paper, we use a combination of acoustic impedance and production data for history matching the full Norne Field. The purpose of the paper is to illustrate a robust and flexible work flow for assisted history matching of large data sets. We apply an iterative ensemble-based smoother, and the traditional approach for assisted history matching is extended to include updates of additional parameters representing rock clay content, which has a significant effect on seismic data. Further, for seismic data it is a challenge to properly specify the measurement noise, because the noise level and spatial correlation between measurement noise are unknown. For this purpose, we apply a method based on image denoising for estimating the spatially correlated (colored) noise level in the data. For the best possible evaluation of the workflow performance, all data are synthetically generated in this study. We assimilate production data and seismic data sequentially. First, the production data are assimilated using traditional distance-based localization, and the resulting ensemble of reservoir models is then used when assimilating seismic data. This procedure is suitable for real field applications, because production data are usually available before seismic data. If both production data and seismic data are assimilated simultaneously, the high number of seismic data might dominate the overall history-matching performance.
The noise estimation for seismic data involves transforming the observations to a discrete wavelet domain. However, the resulting data do not have a clear spatial position, and the traditional distance-based localization schemes used to avoid spurious correlations and underestimated uncertainty (because of limited ensemble size), are not possible to apply. Instead, we use a localization scheme that is based on correlations between observations and parameters that does not rely on physical position for model variables or data. This method automatically adapts to each observation and iteration.
The results show that we reduce data mismatch for both production and seismic data, and that the use of seismic data reduces estimation errors for porosity, permeability, and net-to-gross ratio (NTG). Such improvements can provide useful information for reservoir management and planning for additional drainage strategies.
The operator experienced an unusual casing failure at a producing SAGD (steam assisted gravity drainage) oil well in summer of 2017. The subject well in the Firebag SAGD field of NE Alberta, Canada had operated successfully for over 11 years. Once the problem was identified, the well was shut in to determine the nature of the failure and options for repair and recovery so it could be returned to operation as soon as possible.
Tasks included identifying and isolating the failure, establishing the cause and nature of the failure, and determining viable repair options. Logging diagnostics to measure/image the failure were performed, which included new ultra-sonic logging imaging technology, high-resolution multi-finger caliper logging, a downhole camera run and conventional eddy flux casing inspection log. Historical log data was also reviewed to assess whether the failure evolved over time, or if the mechanism was acute. Once the nature of the failure was established, the optimal repair method was chosen, planned and carried out.
Sophisticated analysis of multi-finger caliper log data, camera images and new technology in the form of an ultrasonic imaging tool for the casing were utilized and are presented. A discussion of potential root cause mechanisms for thermal wells is provided, including a variety of failure modes that could be ruled out. Confidence in the failure mode specific to this well was increased by considering information acquired from multiple diagnostic tools. The nature of the connection failure determined from this process is outlined, along the rationale behind the repair method selected to remediate the well.
Luo, Xiaodong (International Research Institute of Stavanger) | Lorentzen, Rolf J. (International Research Institute of Stavanger) | Valestrand, Randi (International Research Institute of Stavanger) | Evensen, Geir (International Research Institute of Stavanger and Nansen Environmental and Remote Sensing Center)
Ensemble-based methods are among the state-of-the-art history-matching algorithms. However, in practice, they often suffer from ensemble collapse, a phenomenon that deteriorates history-matching performance. It is customary to equip an ensemble history-matching algorithm with a localization scheme to prevent ensemble collapse. Conventional localization methods use distances between the physical locations of model variables and observations to modify the degree of the observations’ influence on model updates. Distance-based localization methods work well in many problems, but they also suffer from dependence on the physical locations of both model variables and observations, the challenges in dealing with nonlocal and time-lapse measurements, and the nonadaptivity to handling different types of model variables.
To enhance the applicability of localization to various history-matching problems, we adopt an adaptive localization scheme that exploits the correlations between model variables and simulated observations. We elaborate how correlation-based adaptive localization can overcome or mitigate issues arising in conventional distance-based localization.
To demonstrate the efficacy of correlation-based adaptive localization, we adopt an iterative ensemble smoother (iES) with the proposed localization scheme to history match the real production data of the Norne Field model, and we compare the history-matching results with those obtained by using the iES with distance-based localization. Our study indicates that when compared with distance-based localization, correlation-based localization not only achieves close or better performance in terms of data mismatch, but also is more convenient to use in practical history-matching problems. As a result, the proposed correlation-based localization scheme might serve as a viable alternative to conventional distance-based localization.
ABSTRACT: Accurate prediction of softening and failure behavior of rocks are essential to hydraulic fracturing simulation using strain-softening type models. Failure to preserve the fracture energy causes these continuums based numerical models to suffer from mesh-size dependency. The virtual Multi-dimensional Internal Bond Model (VMIB) is derived from a particle-based constitutive law at the micro scale. It has been implemented in a 3D Finite Element Method in which material softening and energy dissipation occur over the “representative elementary volume”. However, in realistic materials, energy dissipation is due to fracture surfaces creation instead of material softening in the element. In this work we present an improved VMIB model to bridge the energy dissipation over the representative elementary volume and the fracture surfaces using a virtual bond potential that incorporates the material fracture energy to eliminate the mesh-size sensitivity. The virtual bond potential considers both the critical fracture energy and element size. The 3D model is calibrated and verified by carrying out simulations of a group of three-point-bend tests using different mesh sizes. Then, by incorporating a three-dimensional element partition method, the model is applied to a series of laboratory scale hydraulic fracturing experiments. Furthermore, multiple hydraulic fracturing from closely-staged clusters is simulated. It is found that the model can accurately capture the fractures growth pattern that is influenced by the stress boundary conditions and the stress shadow interaction among the fractures. The results also show the predicted breakdown pressure reasonably agree with the experiment data.
In hydraulic fracturing simulation, capturing rock mechanical behaviors in response of fluid pressurization is crucial for fracture pattern and fluid pressure prediction. Many analytical solutions [Sneddon, 1946; Sneddon and Elliot,1946; Khristianovic and Zheltov, 1955; Nordgren, 1972, Geertsma and de Klerk, 1969] based on linear elastic fracture mechanics (LEFM) have been proposed to analyze different mechanisms of fluid-driven fracturing. However, the complexity of hydraulic fractures brings challenge to classic model due to the constitution features of rock such as heterogeneity and nonlinearity. Therefore, to solve the complex fracturing problem, many numerical methods have been proposed. The Displacement Discontinuity Method (DDM) [Kumar, 2013; Weng et al., 2011; Sesetty and Ghassemi, 2012, 2013, 2017; Farmahini-Farahani and Ghassemi, 2015; Verde and Ghassemi, 2013] has been widely used in fracture mechanics, hydraulic fracturing and natural fracture networks interaction, especially in large scale problem and multiple complex fracture network simulation. Discrete Element Method (DEM) is an effective method of addressing engineering problems in granular materials. This particle-based method is an ideal tool to simulate rock failure and has been used to simulate the hydraulic fracture propagation [Damjanac et al., 2010; Deng, Podgorney and Huang, 2011]. Continuum based method associated with strain softening model has been widely used to capture the nonlinear mechanical behaviors of rocks. Coupled damage model have also been propsoed [Min et al., 2011; Min, 2013; Huang and Ghassemi, 2016] to simulate mixed-mode hydraulic fracture propagation. [Gao and Ghassemi, 2017] developed a 3D FEM model to analyze the pressurized fracture problem in heterogeneous rock. By introducing discontinuity into continuum Finite Element Method (FEM), the extended finite element method (XFEM) [Belytschko and Black, 1999; Moes et al., 1999] has also been implemented in hydraulic fracturing modeling [Gordeliy and Peirce, 2013, Dahi-Taleghani and Olson 2011, Wang 2015, Shi et al, 2017]. Phase field model [Moelans et al., 2008] is a versatile technique for solving interfacial problems at the mesoscale and has been employed to solve the fluid driven fracture propagation in porous media in small scale [Mikelic et al., 2015]. The Virtual Multidimensional Internal Bond (VMIB) is a particle based constitutive model that was proposed by [Zhang and Ge, 2005, 2006] to capture the macroscale material properties from the mechanical response of microscale particles and bonds. VMIB was used in [Huang et al., 2013] to simulate 3D mix-mode hydraulic fracture propagation. Recently, more hybrid methods have been proposed such as Finite- discrete element method [Zhao et al., 2014] and DDM-FEM [Kumar and Ghassemi, 2016] have been used for hydraulic fracturing simulation, which integrate the advance features of each method.
The indoor testing system for small tank was designed to study and experiment the autonomous vessel. Such system was able to serve both engineering and academic purposes; the system was included water tank, vessel localization system based on Wiimote IR camera, test vessel, and a monitoring computer. Low-cost semi-submersible platform modelled with over-actuated thrusts configuration was built to serve as test vessel; this was able to move on the water in 3 degrees of freedoms: surge, sway, and yaw axis. The tiny computer, called Raspberry Pi 3, was used to manipulate the vessel and communicated with a monitoring computer. An IMU sensor was also included for provide the accurate dynamics data as an option. Four high power infrared light sources were located at four corners on the vehicle; these provided vehicle current position during investigation in real-time; moreover, they also defined heading direction, model reference point (bow, port, and starboard side) as body-fixed reference frame. The planar homography transformation technique was used to correct object picture due to perspective distortion and provide real world distance (in metric system). The mathematical model without disturbances was also described: low speed dynamic model, kinematic model and actuator allocation. The proposed system was tested by performing system identification with open-loop step response method.
Due to a great interest in artificial intelligence technology, the development and research in autonomous systems have been grown. Replacing machines over human in industries are dramatically increasing, and expanding to marine applications. Overall of marine autonomous system must be carefully designed. Especially, the vessel behavior insight is important because it is a key to designs and implements the control algorithm. Fossen (1994), and Skjetne et al. (2004) have described a general 6 degree-of-freedom for vessel model. Furthermore, there are another model that influenced the vessel and must be concerned such as thruster configuration models and disturbances models.
History matching is a widely used reservoir simulation workflow. Its goal is to create models which reasonably match historical field injection and production data so future predictions can be made. Many methods have been developed in the past to try to solve this problem. One set of methods that have been those that involve ensemble data assimilation. An example is the Ensemble Kalman Filter (EnKF), which has been widely implemented. A key issue with ensemble methods is that Under sampling can severely degrade the reliability of the estimation. In this paper we introduce a new method to improve the result quality in ensemble data assimilation methods.