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Energy sources are vital to sustain and grow the world economy. As of today, the world mainly relies on fossil fuel as the source of energy for transportation, power generation, chemicals manufacturing, and other industrial applications. The conventional sources of hydrocarbon are steadily declining; however, the oil and gas industry has been successful in finding unconventional hydrocarbons, such as heavy oil and shale gas. There are distinct challenges in producing and processing the hydrocarbons from unconventional sources into usable end products. Reducing the footprint during the production of oil, refined products, and gas will benefit the industry by reducing the overall cost and improving the health, safety, and environmental impact.
The objective of this study is to develop a hybrid model by combining physics and data-driven approach for unconventional field development planning. We used physics-based reservoir simulations to generate training datasets. These uncalibrated priors were incorporated into data-driven machine learning (ML) algorithms so that the algorithms can learn the underlying physics from reservoir simulation input and output. The ML model is trained such that it provides fast and scalable applications with good accuracy to find optimum unconventional field development, accounting for geological properties, completions design, well spacing and child well timing.
We trained ML models with reservoir simulation inputs and cumulative oil production for parent and child wells. A single half-cluster reservoir model was built where fracture propagation is simulated with pressure-dependent fracture properties and a child-well is introduced with different timing and well spacing. After performing a sensitivity analysis to reduce the number of training inputs, more than 20,000 simulations results were generated as the training data. The best accuracy, R2=0.94, was achieved with the neural network model after tuning hyper-parameters. Then, we incorporated the trained model with the genetic algorithm to perform efficient history matching to calibrate model parameters.
The hybrid model, physics-embedded machine learning model, is extremely efficient that it takes several minutes to complete a single well history matching. The prediction from the history-matched hybrid model is physically meaningful showing that it properly captures the impact of fracture geometry, child well spacing, and timing on production. With the multiple history matching results, we populated spatial distribution of estimated ultimate recovery (EUR) and calibrated model parameters. To validate the workflow, a blind test was conducted on selected areas from US onshore field. The model prediction with the populated parameters was found to be in good agreement with the actual production history indicating the predictive capability of the hybrid approach.
The proposed model can provide quick and scalable solutions that honors underlying physics to help decision making on unconventional field development. The model can capture interactions between wells including production degradation due to child-well effect. By calibrating model input parameters over the entire basin, we can predict EUR, yearly cumulative oil followed by economic metrics such as NPV10 at any location in the basin. The impact of different completion design (e.g., fluid intensity, cluster spacing) on production profile and economic matrices is also quickly assessed.
Summary Proper characterization of heterogeneous rock properties and hydraulic fracture parameters is essential for optimization of well spacing and reliable estimation of estimated ultimate recovery (EUR) in unconventional reservoirs. We propose a novel reservoir model parameterization method to reduce the number of unknowns, regularize the ill-posed problem, and enhance the efficiency of history matching of unconventional reservoirs. Typically, hydraulic fractures are represented with much higher resolution through local grid refinements compared to the matrix properties. In our approach, the spatial property distribution of both matrix and fractures is represented using a few parameters via a linear transformation with multiresolution basis functions. The parameters in transform domain are then updated during model calibrations, substantially reducing the number of unknowns. The multiresolution basis functions are constructed by using Eigen-decomposition of an adaptively coarsened grid Laplacian corresponding to the data resolution. Higher property resolution at the area of interest through the adaptive resolution control while keeping the original grid structure improves quality of history matching, reduces simulation runtime, and improves the efficiency of history matching. We demonstrate the power and efficacy of our method using synthetic and field examples. First, we illustrate the effectiveness of the proposed multiresolution parameterization by comparing it with traditional methods. For the field application, an unconventional tight oil reservoir model with a multistage hydraulic fractured well is calibrated using bottomhole pressure and water cut history data. The hydraulic fractures as well as the stimulated reservoir volume (SRV) near the well are represented with higher grid resolution. The calibrated ensemble of models are used to obtain bounds of production forecast. Our proposed method is designed to calibrate reservoir and fracture properties with higher resolution in regions that have improved data resolution and higher sensitivity to the well performance data, for example the SRV region and the hydraulic fractures. This leads to a fast and efficient history matching workflow and enables us to make optimal development/completion plans in a reasonable time frame.
Soroush, Mohammad (RGL Reservoir Management, University of Alberta) | Roostaei, Morteza (RGL Reservoir Management) | Fattahpour, Vahidoddin (RGL Reservoir Management) | Mahmoudi, Mahdi (RGL Reservoir Management) | Keough, Daniel (Precise Downhole Services Ltd) | Cheng, Li (University of Alberta) | Moez, Kambiz (University of Alberta)
Accurate prediction of flow regime and flow profile in wellbore is among the main interests of production engineers in the quest of optimizing wellbore production and increasing reliability of downhole completion tools especially in SAGD projects. This study introduces a methodology for wellbore monitoring by detecting flow phase and flow regime. In order to develop this method, an advanced multi-phase flow injection experiment was designed and commissioned.
A flow injection setup was developed to test distributed fiber optic sensor installation under different operating conditions, including multi-phase flow (oil, brine and gas), and flow fraction scenarios. Different signal processing methods were applied to extract meaningful features and filter the noise from the raw signals. A statistical analysis was performed to assess the trend of the driven data. Then, typical SAGD models were simulated to assess the results of experimental setup for scale-up purpose and determination of local breakthrough of steam along the well.
Results showed that the Distributed Acoustic Sensing (DAS) data contains different levels of signals for each phase and flow regime. We also found that some level of uncertainties is involved in relating the flow regime and DAS information which could be resolved by improving the sensor installation procedure. In addition, the application of data-driven machine learning methods was found necessary to interpret the signal patterns. Initial results have shown that steam breakthrough along the well can be detected using real time DAS high energy/frequency signals. It can be concluded that including the DAS along with Distributed Temperature Sensing (DTS) is necessary to provide a better picture of steam conformance and SAGD wellbore monitoring. The limitations of the current experimental setup restricted further conclusions regarding the hybrid DAS and DTS application.
This paper is a part of an ongoing project to address the application of the combined DAS and DTS in SAGD projects. The ultimate goal is a downhole monitoring system to oversee the flow phase, flow regime and sand ingress in thermal application. The next phase will address the required improvements for developing a flow loop to handle high temperatures, include sand production and mimic thermal operation conditions.
Steam Assisted Gravity Drainage (SAGD) is a complex process and often requires more control relative to conventional applications during production operations. Flow Control Devices (FCDs) have been identified as a technology that offers improved efficiency of the process while simplifying the operations. The first FCD completions were installed in SAGD wells in Canada over a decade ago with the intention of improving the steam chamber conformance and reducing the steam-oil ratio (SOR). While it is widely understood that FCD completions, for the most part, have helped achieve the desired uplift for SAGD producers, further optimization could be made on future completion designs and operation strategy by looking at actual performance data from previous installations. The objective of the study was to obtain key design parameters and considerations for future FCD completion designs.
The majority of FCD completions in MacKay River were tubing deployed, installed in previously producing wellbores (retrofit). This study looks at 11 wells that were completed with a Baker Hughes FCDs. The analysis was broken down into 2 segments: production analysis and modelling. Production strategy implemented for each well was taken into account to eliminate variances. The modelling used a combination of steady state simulation (presented in this paper) and numerical simulation (to be presented in part II).
The study showed that TD FCDs improve the performance of SAGD well pairs when implemented in the appropriate candidate wells. An important outcome was the development of a candidate wells’ selection criteria, to ensure the retrofit completion improved performance and did not exacerbate other problems. Furthermore, design consideration were identified to improve the performances of future TD FCD installations.
The seismic inversion method using the seismic onset times has shown great promise for integrating real- continuous seismic surveys for updating geologic models. However, due to the high cost of seismic surveys, such frequent seismic surveys are not commonly available. In this study, we focus on analyzing the impact of seismic survey frequency on the onset time approach, aiming to extend the advantages of onset time approach when infrequent seismic surveys are available.
To analyze the impact of seismic survey frequency on the onset time approach, first, we conduct a sensitivity analysis based on the frequent seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. The calculated onset time maps based on seismic survey data sampled at various time intervals from the frequent data sets are compared to examine the need and effectiveness of interpolation between surveys. Additionally, we compare the onset time inversion with the traditional seismic amplitude inversion and quantitatively investigate the nonlinearity and robustness for these two inversion methods.
The sensitivity analysis shows that using interpolation between seismic surveys to calculate the onset time an adequate onset time map can be extracted from the infrequent seismic surveys. This holds good as long as there are no changes in the underlying physical mechanisms during the interpolation period. A 2D waterflooding case demonstrates the necessity of interpolation for large time span between the seismic surveys and obtaining more accurate model update and efficient data misfit reduction during the inversion. The SPE Brugge benchmark case shows that the onset time inversion method obtains comparable permeability update as the traditional seismic amplitude inversion method while being much more efficient. This results from the significant data reduction achieved by integrating a single onset time map rather than multiple sets of amplitude maps. The onset time approach also achieves superior convergence performance resulting from its quasi-linear properties. It is found that the nonlinearity of the onset time method can be smaller than that of the amplitude inversion method by several orders of magnitude.
Olalotiti-Lawal, Feyi (Texas A&M University) | Onishi, Tsubasa (Texas A&M University ) | Kim, Hyunmin (Texas A&M University ) | Datta-Gupta, Akhil (Texas A&M University ) | Fujita, Yusuke (JX Nippon Oil & Gas Exploration Corporation) | Hagiwara, Kenji (JX Nippon Oil & Gas Exploration Corporation)
We present a simulation study of a mature reservoir for carbon dioxide (CO2) enhanced-oil-recovery (EOR) development. This project is currently recognized as the world’s largest project using post-combustion CO2 from power-generation flue gases. With a fluvial formation geology and sharp hydraulic-conductivity contrasts, this is a challenging and novel application of CO2 EOR. The objective of this study is to obtain a reliable predictive reservoir model by integrating multidecadal production data at different temporal resolutions into the available geologic model. This will be useful for understanding flow units along with heterogeneity features and their effect on subsurface flow mechanisms, to guide the optimization of the injection scheme and maximize CO2 sweep and oil recovery from the reservoir.
Our strategy consists of a hierarchical approach for geologic-model calibration incorporating available pressure and multiphase production data. The model calibration is performed using regional multipliers, and the regions are defined using a novel adjacency-based transform accounting for the underlying geologic heterogeneity. The genetic algorithm (GA) is first used to match 70-year pressure and cumulative production by adjusting pore volume (PV) and aquifer strength. Water-injection data for reservoir pressurization before CO2 injection is then integrated into the model to calibrate the formation permeability. The fine-scale permeability distribution consisting of more than 7 million cells is reparameterized using a set of linear-basis functions defined by a spectral decomposition of the grid-connectivity matrix (Laplacian grid). The parameterization represents the permeability distribution using a few basis-function coefficients that are then updated during history matching. This leads to an efficient and robust work flow for field-scale history matching.
The history-matched model provided important information about reservoir volumes, flow zones, and aquifer support that led to additional insight compared with previous geological and simulation studies. The history-matched field-scale model is used to define and initialize a detailed fine-scale model for a CO2 pilot area that will be used to study the effect of fine-scale heterogeneity on CO2 sweep and oil recovery. The uniqueness of this work is the application of a novel geologic-model parameterization and history-matching work flow for modeling of a mature oil field with decades of production history, and which is currently being developed with CO2 EOR.
Baek, Seunghwan (Texas A&M University) | Akkutlu, I. Yucel (Texas A&M University) | Lu, Baoping (Sinopec Research Institute for Petroleum Engineering) | Ding, Shidong (Sinopec Research Institute for Petroleum Engineering) | Xia, Wenwu (Harding Shelton Petroleum Engineering & Technology Limited)
Routine history-matching and reservoir calibration methods for horizontal wells with multiple hydraulic fractures are complex. Calibration of important fracture and matrix quantities is, however, essential to understand the reservoir and estimate the future recoveries. In this paper, we propose a robust method of simulation-based history-matching and reserve prediction by incorporating an analytical solution of production Rate Transient Analysis (RTA) as an added constraint. The analytical solution gives the fracture surface area contributing to the drainage of the fluids from the matrix into the fractures. The surface area obtained from the RTA is the effective area associated with the production—not total area. It is the most fundamental and the most significant quantity in the optimization problem. Differential evolution (DE) algorithm and a multi-scale shale gas reservoir flow simulator are used during the optimization. We show that the RTA-based optimization predicts the quantities related to completion design significantly better. Further, we show how the estimated total fracture surface area can be used to measure the hydraulic fracturing quality index, as an indication of the quality of the well completion operation. The most importantly, we predict that the fractures under closure stress begin to close much sooner (100 days) than the prediction without the RTA-based fracture surface area constraint. The deformation continues under constant closure stress for about 20 years, when the fractures are closed nearly completely. This work attempts to use the traditional reservoir optimization technologies to predict not only the reserve but also the life of the unconventional well.
Proper characterization of heterogeneous rock properties and hydraulic fracture parameters is essential for optimizing well spacing and reliable estimation of EUR in unconventional reservoirs. High resolution characterization of matrix properties and complex fracture parameters requires efficient history matching of well production and pressure response. We propose a novel reservoir model parameterization method to reduce the number of unknowns, regularize the ill-posed problem and enhance the efficiency of history matching of unconventional reservoirs.
Our proposed method makes a low rank approximation of the spatial distribution of reservoir properties taking into account the varying model resolution of the matrix and hydraulic fractures. Typically, hydraulic fractures are represented with much higher resolution through local grid refinements compared to the matrix properties. In our approach, the spatial property distribution of both for matrix and fractures is represented using a few parameters via a linear transformation with multiresolution basis functions. The parameters in transform domain are then updated during model calibrations, substantially reducing the number of unknowns. The multiresolution basis functions are constructed by eigen-decomposition of an adaptively coarsened grid Laplacian corresponding to the data resolution. High property resolution at the area of interest through the adaptive resolution control while keeping the original grid structure improves quality of history matching, reduces simulation runtime and improves the efficiency of history matching.
We demonstrate the power and efficacy of our method using synthetic and field examples. First, we illustrate the effectiveness of the proposed multiresolution parameterization by comparing it with traditional method. For the field application, an unconventional tight oil reservoir model with a multi-stage hydraulic fractured well is calibrated using bottom-hole pressure and water cut history data. The hydraulic fractures as well as the stimulated reservoir volume (SRV) near the well are represented with higher grid resolution. In addition to matrix and fracture properties, the extent of the SRV and hydraulic fractures are also adjusted through history matching using a Multiobjective Genetic Algorithm. The calibrated ensemble of models are used to obtain bounds of production forecast.
Our proposed method is designed to calibrate reservoir and fracture properties with higher resolution in regions that have improved data resolution and higher sensitivity to the well performance data, for example the SRV region and the hydraulic fractures. This leads to a fast and efficient history matching workflow and enables us to make optimal development/completion plans in a reasonable time frame.
Salehi, Amir (Quantum Reservoir Impact International LLC) | Hetz, Gill (Quantum Reservoir Impact International LLC) | Olalotiti, Feyisayo (Quantum Reservoir Impact International LLC) | Sorek, Nadav (Quantum Reservoir Impact International LLC) | Darabi, Hamed (Quantum Reservoir Impact International LLC) | Castineira, David (Quantum Reservoir Impact International LLC)
An integral aspect of smart reservoir management of oil and gas fields is the process of identifying and performance forecasting of the remaining, feasible, and actionable field development opportunities (FDOs). In the present work, we introduce an adaptive full-physics simulation-based forecasting framework that applies a series of cutting-edge technologies to provide short- and long-term forecasts for both field- and well-level performance. Our workflow can be applied to a comprehensive opportunities inventory including behind-pipe recompletion, infill drilling, and sidetrack opportunities. In our approach, we begin with a model order reduction technique, which involves a parsimonious elimination of redundancies existing in a given geologic model. This involves an adaptive model upscaling strategy that retains fine details in the vicinity of critical geological features by locally varying the resulting model grid resolution. Reduced models, which are validated using streamline-based flow metrics, are passed into an automated sensitivity study and model calibration engine for efficient reconciliation of observed production trends in the field. Here, we apply a recently proposed Ensemble Smoother robust Levenberg- Marquardt (ES-rLM) method to generate plausible model realizations that replicate the reservoir energy. Representative models are further improved in a sensitivity-based local inversion step to match multiphase production data at the well level. An approach alternative to streamlines, which is compliant with a general unstructured grid format, is utilized to directly compute production data sensitivities on the underlying grid in the local inversion module. Finally, calibrated models are directly passed to the optimization and forecasting engine to assess and optimize field opportunities and development scenarios. This framework has been successfully applied to several giant mature assets in the Middle East, North America, and South America. A case study for one of the giant reservoirs in Latin America is presented where hundreds of field development opportunities are initially identified. We then apply our forecasting framework to the various scenarios including all opportunities to deliver the optimum field development plan. We propose a systematic workflow for field-scale modeling and optimization using an adaptive framework. Our approach facilitates a flexible framework to rapidly generate reliable forecasts and quantify associated uncertainties in a robust manner. This advantage in flexibility and robustness is tied to our fast and automated two-stage model calibration module that leads to substantial savings in computational time. This makes it an efficient method for quantifying the uncertainty as demonstrated through improved estimation of the faults’ connectivity, permeability distribution, fluid saturation evolution, and swept volume.