Rock strength evaluation is commonly performed on extracted core samples. The retrieval process of core samples is costly and with high risk, especially in unconventional wells. Besides, mechanical testing carried out on core samples could be time-consuming, particularly for low permeability rocks such as shales. In contrast, tons of shale cuttings are generated and discarded at the end of drilling. Small scale testing, such as instrumented indentation test, could be performed on shale cuttings to obtain the mechanical properties of shale rocks. Unlike core samples, however, the bedding orientation, critical to the definition of mechanical properties of shale cuttings as transversely isotropic material, is indistinguishable on cuttings. A methodology has been developed in the following work to deduce the mechanical properties based on indentation testing of randomly oriented artificial shale cuttings.
Estimation of shale cuttings’ elastic constants was carried out using microindentation and constrained inverse algorithm developed based on contact mechanics solutions. Microindentation testing was performed on multiple oriented artificial shale cuttings to obtain the indentation modulus as the function of the unknown bedding orientation. The contact mechanics solutions for both transversely isotropic and anisotropic material were utilized to correlate the indentation modulus to the stiffness tensor components of transversely isotropic shale. An inverse problem was formulated with imposed constraints to identify the mean values of the quantities of interest that best fit the data. The constraints represent the physical information about the bounds on elastic properties as well as a mathematical constraint on the structure of elasticity tensor ensuring the accuracy and robustness of the solutions to this optimization problem. Lastly, Ultrasonic Pulse Velocity (UPV) test was performed to validate the modeling results and good agreement was found between the results of the experimental and modeling efforts and results from UPV tests performed on the same material.
Mechanical properties of shale rocks hold great importance in the design and implementation of drilling and production programs. Retrieval of traditional core samples is known to be expensive and risky as a failure in the recovery process could lead to well abandonment. By using several cuttings with unknown varying bedding orientations, the elastic constants of shales were inferred based on the microindentation testing and the algorithm developed in this work. Successful implementation of this work would allow for a more efficient and economical mechanical characterization of shales.
The growing popularity of model-based optimization work flows has resulted in an increase in their application to field cases. This paper presents an unbiased stochastic data-driven work flow in which surface and subsurface uncertainties are accounted for and their effects on facilities design and operational decisions are quantified. Three-dimensional reservoir models are best created with a combination of well logs and 3D-seismic data. However, the effective integration of these results is not easy because of limited seismic resolution.
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.
Geophysical Reservoir Monitoring GRM systems such 4D seismic are increasingly used in the oil and gas industry because they provide unique and useful information on fluid movement within the reservoir. This information is relevant for many reservoir management decisions; including new well placement, well intervention, and reservoir model updating.
Unfortunately, it has been difficult to estimate the value creation of any data acquisition scheme due to the fact that a multidisciplinary approach is required to model the value that future measurements will imply in future decisions. This assessment requires a common decision making simulation frame work that can integrate the input from geo-modelers, geophysicist and reservoir engineers.
This work presents an example of how a Close Loop Reservoir Management (CLRM) simplification can be used as a framework for simulating NPV changes due to assimilation of production and saturations in a simple toy model. It combines state-of-the-art data assimilation and uncertainty modeling methods with a robust optimization genetic algorithm to calculate NPV improvements due to model update and its relationship with the NPV obtained from the synthetic reservoir.
In this context a simple synthetic model is presented. It recreates a segment of green field under a strong aquifer influence with two discovery wells. The reservoir development requires the selection of 4 well locations at fixed drilling times. The development strategy selection is obtained with the use of a genetic algorithm within the CLRM framework. Subsequently two cases are presented: one of assimilating only production after the first two wells have been drilled, just before deciding the locations of the last two wells; and a second case, in which production and saturation are assimilated at the same time. The saturation map assimilated is assumed to be output of a 4D seismic acquisition. The model update imposes the need of optimally relocate the last two wells which results in a NPV change.
The results show how the obtained NPVs is incremented by the relocation of the last two wells in both cases. A bigger increment is obtained when both, production and saturation are assimilated. In addition, the ensemble improved its forecast capability the most, when saturation assimilation is included. Nevertheless, the ensemble expected NPV decreases after assimilation from the value obtained from the first development strategy optimization; this indicates an optimistic early NPV valuation due to the initial ensemble distributions spread.
The study presents an asset simulation framework that could be used to evaluate data acquisition investments through the systematic modeling of reservoir uncertainties with in a decision oriented focus. This could include the inclusion of additional uncertain model parameters, the insertion of water injector and well conversions, the assimilation of saturations at different intervals, the change on the quality of the saturation maps assimilated, in addition to sensitivity studies of other economic constrains.
Hydraulic fracturing is carried out in most shale gas fields to enhance reservoir permeability. Thousands of microseismic events are observed with geophone arrays during fracking stages. Microseismic hypocenter distributions are essential information to delineate stimulated reservoir volume (SRV), and there are some useful automatic processing tools to get hypocenter locations. However, recorded seismograms contain noise and complex phases that cause ambiguity in the data processing. In addition, the velocity model selected has a great influence on the accuracy of the hypocenter information.
In this research, to enhance the accuracy of microseismic event hypocenter locations, we picked P and S phases using an array seismogram volume (ASV). The ASV consists of shot gathers and receiver gathers of selected events and receivers. The events should be limited to those that have enough waveform similarity. Once we pick an onset time of a single trace in the ASV, the picked seed trace is compared with neighboring traces using cross correlation; most of the onset times in the ASV are picked automatically. Because the process requires picking of single traces, we call this procedure semi-auto picking. This semi-auto picking tool can reduce the time needed for manual picking and the process is efficient and provides high-quality results.
Shale sediment are frequently characterized by anisotropic parameters. The sediments in Barnett, our study area, also show anisotropy. Therefore, an accurate velocity model is required to achieve precise microseismic event analysis. A tilted layer orthorhombic velocity model was adopted in this study. P-wave sonic and gamma ray logs were available for a reference well located close to the study area. The 3D seismic survey interpretation provided a tilted layer angle. The principal horizontal stress direction was provided by a previous study in Barnett. Using this geoscience information as constraints, a number of optimized parameters could be reduced. Seven Thomsen parameters and the Vp/Vs ratio of the defined layers were optimized using perforation shots.
A grid search location approach was applied to locate 932 events during a single fracking stage. To obtain final locations, the following objective functions were combined: (1) P, Sh, and Sv travel time misfits; (2) travel time differences between receivers; (3) travel time differences between different wave phases; (4) travel time differences between microseismic events and master events (perforation shots); (5) P phase polarization; and (6) P phase polarization differences between microseismic and master events. Spatio-temporal behavior of the located event cloud was investigated using R-T plot analysis. The SRV was observed to grow with the injection volume in this stage and the effective fracture thickness was almost 10 mm after treatment.
Wang, Wendong (China University of Petroleum) | Zhang, Kaijie (China University of Petroleum) | Su, Yuliang (China University of Petroleum) | Tang, Meirong (PetroChina Oil & Gas Technology Research Institute of Changqing Oil field) | Zhang, Qi (China University of Geosciences) | Sheng, Guanglong (China University of Petroleum)
In the development of shale oil and gas reservoir, hydraulic fracture treatments may induce complex network configuration, which is very challenging to characterize. The existing fracture properties interpretation methods mostly rely on simplifying assumptions and are typically empirical in nature. The aim of this work is therefore to introduce an integrated framework involving fractal theory, inverse analysis of micro-seismic events (MSE), and rate-transient analysis to map the heterogeneity and distribution of fracture properties. In this work, a general framework is proposed to characterize both the geometry configuration and the owing properties of the complex fracture network (CFN). The CFN characterization framework is naturally divided into two stages: characterize the fracture geometry network by microseismic data and characterize the fracture dynamic properties by production data. In the fracture configuration characterization stage, a stochastic fractal fracture model based on an L-system fractal geometry is applied to describe the CFN geometry. Moreover, the genetic algorithm (GA) as a mixed integer programming (MIP) algorithm are applied to find the most probable fracture configuration based on the microseismic data. As to the owing properties characterization stage, we introduced embedded discrete fracture model (EDFM) for the computational concern and a Bayesian framework is used to quantify these fracture dynamical properties e.g., conductivity, porosity and pressure dependent multiplier by assimilating the production data. In addition, rate-transient analysis is also applied to calibrate the total fracture length and estimate effective stimulated-reservoir volume (ESRV). In order to validate this framework, a synthetic numerical case is developed. The result indicates that our integrated framework is able to characterize both CFN configuration and properties by assimilating microseismic and production data sequentially. The proposed workflow shows that the characterized CFN model would yield reasonable probability predictions in unconventional production rate.
SUMMARY Optimization is an important tool for most geophysical inverse problem. In this work, we propose the time-domain full waveform inversion (FWI) using truncated Newton optimization, meanwhile we indirectly gain the knowledge of Hessian matrix information through Hessian-vector products calculated by 2nd-order central finite-difference approximation, and the above products can be obtained with no difficulty by gradients of two adjacent models. The proposed method is a Hessianfree and applicable Newton method while the explicit construction of conventional Hessian or of its inverse is beyond nowaday computational capability. The study of synthetic Marmousi2 model demonstrates that: in terms of computational efficiency and convergence speed, the truncated Newton method preforms better than L-BFGS and other methods in FWI, and the Newton direction or Hessian matrix plays a crucial role in rebuilding the subsurface velocity models, especially in the deep areas. INTRODUCTION Full waveform inversion (Tarantola, 1986; Virieux and Operto, 2009) uses all of the waveform information including amplitude and phase to obtain the high-resolution subsurface velocity model.
The multiscale inversion strategy is widely used to mitigate the cycle-skipping problem in full waveform inversion. There are many different approaches to implement the multiscale inversion and the widely used low-to-high frequency continuation is not applicable when the observed data lack low frequencies. As an alternative to multiple frequencies, offsets continuation is also able to suppress the cycle-skipping problem when the initial model is not perfect. We improve the multiscale strategy of offset-selection by introducing a local similarity criterion. Thus, we formulate an adaptive data-driven selection process that is better than conventional offset continuation approaches. The global-crosscorrelation objective function used here aims to maximize the similarity of two data sets instead of subtracting one from another and it is more consistent with the selection strategy. Besides, the crosscorrelation-based objective function is more sensitive to the phase information of the data and thus is more applicable to field data. We use a modified elastic Marmousi example to verify the effectiveness of the proposed method.
Presentation Date: Thursday, October 18, 2018
Start Time: 8:30:00 AM
Location: 208A (Anaheim Convention Center)
Presentation Type: Oral