Not enough data to create a plot.
Try a different view from the menu above.
The Merriam-Webster Dictionary defines simulate as assuming the appearance of without the reality. Simulation of petroleum reservoir performance refers to the construction and operation of a model whose behavior assumes the appearance of actual reservoir behavior. The model itself is either physical (for example, a laboratory sandpack) or mathematical. A mathematical model is a set of equations that, subject to certain assumptions, describes the physical processes active in the reservoir. Although the model itself obviously lacks the reality of the reservoir, the behavior of a valid model simulates--assumes the appearance of--the actual reservoir. The purpose of simulation is estimation of field performance (e.g., oil recovery) under one or more producing schemes. Whereas the field can be produced only once, at considerable expense, a model can be produced or run many times at low expense over a short period of time. Observation of model results that represent different producing ...
Reservoir simulation is a widely used tool for making decisions on the development of new fields, the location of infill wells, and the implementation of enhanced recovery projects. It is the focal point of an integrated effort of geosciences, petrophysics, reservoir, production and facilities engineering, computer science, and economics. Geoscientists using seismic, well-log, outcrop analog data and mathematical models are able to develop geological models containing millions of cells. These models characterize complex geological features including faults, pinchouts, shales, and channels. Simulation of the reservoir at the fine geologic scale, however, is usually not undertaken except in limited cases.
Using commercial numerical reservoir simulators to build a full-field reservoir model and simultaneously history matching multiple dynamic variables for a highly complex offshore mature field in Malaysia had proved challenging. In the complete paper, the authors demonstrate how artificial intelligence (AI) and machine learning can be used to build a purely data-driven reservoir simulation model that successfully history matches all dynamic variables for wells in this field and subsequently can be used for production forecasting. This synopsis concentrates on the process used, while the complete paper provides results of the fully automated history matching. In the presented technique, which the authors call subsurface analytics, data-driven pattern-recognition technologies are used to embed the physics of the fluid flow through porous media and to create a model through discovering the best, most-appropriate relationships between all measured data in each reservoir. This is an alternative to starting with the construction of mathematical equations to model the physics of the fluid flow through porous media, followed by modification of geological models in order to achieve history match.
Abstract Fracture growth in layered formations with depth-dependent properties has been a topic of interest amongst researchers because of its critical influence on well performance. This paper revisits some of the existing height-growth models and discusses the evaluation process of a new and modified model developed after incorporating additional constraints.The net-pressure is the primary driver behind fracture propagation and the pressure distribution in the fracture plays an important role in vertical propagation, as it supplies the necessary energy for fracture advancement in the presence of opposing forces. The workflow adopted for this study included developing a preliminary model that solves a system of non-linear equations iteratively to arrive at fracture height versus net pressure mapping. The theoretical results were then compared to those available in the literature. The solution set was then extended to a 100-layer model after incorporating additional constraints using superposition techniques.The predicted outcomes were finally compared to the fracture height observations made in the field on several treatments. A reasonable agreement between model-predicted and observed height was observed when a comparison between the two was made, for most cases.The majority of these treatments were pumped in vertical wells, at low injection rates of up to 8.0 bbl/min (0.021 m/s) where net pressures were intentionally restricted to 250 psi (1.72 MPa) in order to prevent fracture rotation to the horizontal plane.The leak-off was minimal given the low permeability formations. In some cases, however, the pumping parameters and fluid imparted pressure distribution appeared to dominate. Overall, it was apparent that for a slowly advancing fracture front, which is the case in low injection rate treatments, the fracture height could be predicted with reasonable accuracy. This condition could also be met in high rate treatments pumped down multiple perforation clusters such as in horizontal wells, though fracture-height measurement may not be as straightforward as in vertical wells. The model developed under the current study is suitable for vertical wells where fracture treatments are pumped at low injection rates. The solid-mechanics solution that is presented here is independent of pumping parameters and can be readily implemented to assist in selection of critical design parameters prior to the job, with a wide range of applicability worldwide.
Abstract Chemical flooding has been widely used to enhance oil recovery after conventional waterflooding. However, it is always a challenge to model chemical flooding accurately since many of the model parameters of the chemical flooding cannot be measured accurately in the lab and even some parameters cannot be obtained from the lab. Recently, the ensemble-based assisted history matching techniques have been proven to be efficient and effective in simultaneously estimating multiple model parameters. Therefore, this study validates the effectiveness of the ensemble-based method in estimating model parameters for chemical flooding simulation, and the half-iteration EnKF (HIEnKF) method has been employed to conduct the assisted history matching. In this work, five surfactantpolymer (SP) coreflooding experiments have been first conducted, and the corresponding core scale simulation models have been built to simulate the coreflooding experiments. Then the HIEnKF method has been applied to calibrate the core scale simulation models by assimilating the observed data including cumulative oil production and pressure drop from the corresponding coreflooding experiments. The HIEnKF method has been successively applied to simultaneously estimate multiple model parameters, including porosity and permeability fields, relative permeabilities, polymer viscosity curve, polymer adsorption curve, surfactant interfacial tension (IFT) curve and miscibility function curve, for the SP flooding simulation model. There exists a good agreement between the updated simulation results and observation data, indicating that the updated model parameters are appropriate to characterize the properties of the corresponding porous media and the fluid flow properties in it. At the same time, the effectiveness of the ensemble-based assisted history matching method in chemical enhanced oil recovery (EOR) simulation has been validated. Based on the validated simulation model, numerical simulation tests have been conducted to investigate the influence of injection schemes and operating parameters of SP flooding on the ultimate oil recovery performance. It has been found that the polymer concentration, surfactant concentration and slug size of SP flooding have a significant impact on oil recovery, and these parameters need to be optimized to achieve the maximum economic benefit.
Abstract Full-physics models in history matching and optimization can be computationally expensive since these problems usually require hundreds of simulations or more. We have previously implemented a physics-based data-driven network model with a commercial simulator that serves as a surrogate without the need to build the 3-D geological model. In this paper, we reconstruct the network model to account for complex reservoir conditions of mature fields and successfully apply it to a diatomite reservoir in the San Joaquin Valley (SJV) for rapid history matching and optimization. The reservoir is simplified into a network of 1-D connections between well perforations. These connections are discretized into grid blocks and the grid properties are calibrated to historical production data. Elevation change, saturation distribution, capillary pressure, and relative permeability are accounted for to best represent the mature field conditions. To simulate this physics-based network model through a commercial simulator, an equivalent 2-D Cartesian model is designed where rows correspond to the above-mentioned connections. Thereafter, the history matching can be performed with the Ensemble Smoother with Multiple Data Assimilation (ESMDA) algorithm under a sequential iterative process. A representative model after history matching is then employed for well control optimization. The network model methodology has been successfully applied to the waterflood optimization for a 56-well sector model of a diatomite reservoir in the SJV. History matching result shows that the network model honors field-level production history and gives reasonable matches for most of the wells, including pressure and flow rate. The calibrated ensemble from the last iteration of history matching yields a satisfactory production prediction, which is verified by the remaining historical data. For well control optimization, we select the P50 model to maximize the Net Present Value (NPV) in 5 years under provided well/field constraints. This confirms that the calibrated network model is accurate enough for production forecasts and optimization. The use of a commercial simulator in the network model provided flexibility to account for complex physics, such as elevation difference between wells, saturation non-equilibrium, and strong capillary pressure. Unlike traditional big-loop workflow that relies on a detailed characterization of geological models, the proposed network model only requires production data and can be built and updated rapidly. The model also runs much faster (tens of seconds) than a full-physics model due to the employment of much fewer grid blocks. To our knowledge, this is the first time this physics-based data-driven network model is applied with a commercial simulator on a field waterflood case. Unlike approaches developed with analytic solutions, the use of commercial simulator makes it feasible to be further extended for complex processes, e.g., thermal or compositional flow. It serves as an useful surrogate model for both fast and reliable decision-making in reservoir management.
Soares, Ricardo Vasconcellos (NORCE Norwegian Research Centre and University of Bergen (Corresponding author) | Luo, Xiaodong (email: firstname.lastname@example.org)) | Evensen, Geir (NORCE Norwegian Research Centre) | Bhakta, Tuhin (NORCE Norwegian Research Centre and Nansen Environmental and Remote Sensing Center (NERSC))
Summary In applications of ensemble-based history matching, it is common to conduct Kalman gain or covariance localization to mitigate spurious correlations and excessive variability reduction resulting from the use of relatively small ensembles. Another alternative strategy not very well explored in reservoir applications is to apply a local analysis scheme, which consists of defining a smaller group of local model variables and observed data (observations), and perform history matching within each group individually. This work aims to demonstrate the practical advantages of a new local analysis scheme over the Kalman gain localization in a 4D seismic history-matching problem that involves big seismic data sets. In the proposed local analysis scheme, we use a correlation-based adaptive data-selection strategy to choose observations for the update of each group of local model variables. Compared to the Kalman gain localization scheme, the proposed local analysis scheme has an improved capacity in handling big models and big data sets, especially in terms of computer memory required to store relevant matrices involved in ensemble-based history-matching algorithms. In addition, we show that despite the need for a higher computational cost to perform model update per iteration step, the proposed local analysis scheme makes the ensemble-based history-matching algorithm converge faster, rendering the same level of data mismatch values at a faster pace. Meanwhile, with the same numbers of iteration steps, the ensemble-based history-matching algorithm equipped with the proposed local analysis scheme tends to yield better qualities for the estimated reservoir models than that with a Kalman gain localization scheme. As such, the proposed adaptive local analysis scheme has the potential of facilitating wider applications of ensemble-based algorithms to practical large-scale history-matching problems.
This page provides SPE members access to the April 2021 issue -- digital, pdf, and online. New! JPT Digital Edition Login required on that site; use your SPEorg login Access for those who received print JPT Digital archive of issues back to Jan 2020 is available – scroll down from the current issue cover. These are the papers synopsized in JPT this month. They are available to SPE members only through 31 May 2021. There are also links to them at the bottom of each related synopsis.
This paper presents a step-by-step work flow to facilitate history matching numerical simulation models of hydraulically fractured shale wells. Sensitivity analysis simulations are performed with a coupled hydraulic fracturing, geomechanics, and reservoir simulator. The results are used to develop what the authors term "motifs" that inform the history-matching process. Using intuition from these simulations, history matching can be expedited by changing matrix permeability, fracture conductivity, matrix-pressure-dependent permeability, boundary effects, and relative permeability. The concept of rate transient analysis (RTA) involves the use of rate and pressure trends of producing wells to estimate properties such as permeability and fracture surface area.
History matching is a critical step for dynamic reservoir modeling to establish a reliable, predictive model. Numerous approaches have emerged over decades to accomplish a robust history-matched reservoir model. As geological and completion complexity of oil and gas fields increase, building a fully representative predictive reservoir model can be arduous to almost impossible. The complete paper outlines an approach to history matching that uses artificial intelligence (AI) with an artificial neural network (ANN) and data-driven analytics.