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Hampton, Thomas J. (Consultant) | El-Mandouh, Mohamed (Consultant) | Weber, Stevan (Consultant) | Thaker, Tirth (Computer Modelling Group) | Patel, K.. (Computer Modelling Group) | Macaul, Barclay (Computer Modelling Group) | Erdle, Jim (Computer Modelling Group)
Abstract Mathematical Models are needed to aid in defining, analyzing, and quantifying solutions to design and manage steam floods. This paper discusses two main modeling methods – analytical and numerical simulation. Decisions as to which method to use and when to use them, requires an understanding of assumptions used, strengths, and limitations of each method. This paper presents advantages and disadvantages through comparison of analytical vs simulation when reservoir characterization becomes progressively more complex (dip, layering, heterogeneity between injector/producer, and reservoir thickness).While there are many analytical models, three analytical models are used for this paper:Marx & Langenheim, Modified Neuman, and Jeff Jones.The simulator used was CMG Stars on single pattern on both 5 Spot and 9 Spot patterns and Case 6 of 9 patterns, 5-Spot. Results were obtained using 6 different cases of varying reservoir properties based on Marx & Langenheim, Modified Neuman, and Jeff Jones models.Simulation was also done on each of the 6 cases, using Modified Neuman steam rates and then on Jeff Jones Steam rates using 9-Spot and 5-Spot patterns.This was done on predictive basis on inputs provided, without adjusting or history matching on analog or historical performance.Optimization runs using Particle Swarm Optimization was applied on one case in minimizing SOR and maximize NPV. Conclusion from comparing cases is that simulation is needed for complex geology, heterogeneity, and changes in layering. Also, simulation can be used for maximizing economics using AI based optimization tool. While understanding limitations, the analytical models are good for quick looks such as screening, scoping design, some surveillance, and for conceptual understanding of basic steam flood on uniform geologic properties. This paper is innovative in comparison of analytical models and simulation modeling.Results that quantify differences of oil rate, SOR, and injection rates (Neuman and Jeff Jones) impact on recovery factors is presented.
Abstract The rate of penetration (ROP) was optimized using a particle swarm optimization algorithm for real-time field data to reduce drilling time and increase efficiency. ROP is directly related to drilling costs and is a major factor in determining mechanical specific energy, which is often used to quantify drilling efficiency. Optimization of ROP can therefore help cut down costs associated with drilling. ROP values were chosen from real-time field data, accounting for weight on bit, bit rotation, flow rate variation along with bit wear. A random forest regressor was used to find correlations between the dependent parameters. The parameters were then optimized for the given constraints to find the optimal solution space. The boundary constraints for the ROP function were determined from the real-time data. The function parameters were optimized using a particle swarm optimization algorithm. This is a meta-heuristic model used to optimize an objective function for its maximum or minimum within given constraints. The optimization method makes use of a population of solution particles which act as the particle swarm. These particles move collectively in the given solution space controlled by a mathematical model based on their position and velocity. This model makes use of the best-known solution for each particle and the global best position of the system to guide the swarm towards the optimal solution. The function was optimized for each well, providing optimal ROP values during real-time drilling. A fast drilling optimizer is crucial to automate and streamline the drilling process. This simultaneous optimization of ROP based on real-time data can be implemented during the process thereby increasing the efficiency of drilling as well as reducing the required drilling time.
Abstract This paper deals with debottlenecking approach of complex and integrated system through means of Holistic Modeling for optimizing hydrocarbon evacuation. As prudent operator for the complex network, it is crucial to pursue strategic ideas and innovative concepts to optimize supply demand balance, fulfill contractual obligations to optimize resources to maximize value creation, whilst protecting investment decisions for monetization of the new field development. It therefore necessitates to prioritize system reliability and de-bottlenecking initiatives to implement successful business plans with appropriate timely reconfiguration at various intensities of the network. It is consequently essential to decipher the pain points by performing root cause analysis and troubleshooting to achieve optimal fit for purpose solution by gaining better understanding of network characteristic, supply distribution & operating topology. Paper focus on a bold step change that was commenced to develop an end-to-end Holistic Network Model from well head (fields) to product delivery to scrutinize the network and propose suitable alleviation by appraising the debottleneck requirement at offshore riser collection manifold which serves as integrated facility for multiple hubs and fields. Model was validated with plant information and deployed to yield robust & realistic results. Multiple sensitivity scenarios were accomplished to analyze current riser manifold configuration limitation checks for tie-back of new field such as ullage opportunity, pressure variations, hydraulic fluxes, potential choking of low-pressure wells/fields and prospective blending specifications violations etc. Obstacles across affected manifold could be estimated and its reconfiguration was planned by means of variations in operating philosophy, alterations in the manifold assembly with appropriate manifold debottlenecking recommendation. Analytics of Integrated Network modelling could qualify not only technical obligations but also empower representative economic evaluation for debottlenecking by appending precise requirement in terms of manifold reconfiguration, backed up by appraised data from network model. Model output also assisted to gauze the potential for enhancing network capacity by implementing appropriate reforms to optimize evacuation for new field line ups. Integrated network model developed with an aid of basic network elements can be subjected to estimate vital features for comprehensive network such as pressure and flow across the various nodes in the system. Methodology describes how by developing an integrated network model that summarize the granularity of a highly complex offshore gas network has facilitated to strategize the manifold reconfiguration and appraise debottleneck requirement besides proposing appropriate mitigation. With integrated network modeled on a single platform allows a uniform data transfer from various elements such as fields, facilities, pipelines, gas highways and terminals into the model which assist for network optimization. The situational analysis via modeling could enable the elimination of new dedicated infrastructure for field evacuation leading to CAPEX optimization there by facilitating its optimal monetization. It reveals extensive usage of model with physical boundaries steering decision for project implementation.
Chen, Cunliang (Tianjin Branch of CNOOC China Co., Ltd) | Han, Xiaodong (CNOOC Ltd and China University of Petroleum, Beijing) | Zhang, Wei (Tianjin Branch of CNOOC China Co., Ltd) | Zhang, Yanhui (Tianjin Branch of CNOOC China Co., Ltd) | Zhou, Fengjun (Tianjin Branch of CNOOC China Co., Ltd)
Abstract The ultimate goal of oilfield development is to maximize the investment benefits. The reservoir performance prediction is directly related to oilfield investment and management. The traditional strategy based on numerical simulation has been widely used with the disadvantages of long run time and much information needed. It is necessary to form a fast and convenient method for the oil production prediction, especially for layered reservoir. A new method is proposed to predict the development indexes of multi-layer reservoirs based on the injection-production data. The new method maintains the objectivity of the data and demonstrates the superiority of the intelligent algorithm. The layered reservoir is regarded as a series of single layer reservoirs on the vertical direction. Considering the starting pressure gradient of non-Newtonian fluid flow and the variation of water content in the oil production index, the injection-production response model for single-layer reservoirs is established. Based on that, a composite model for the multi-layer reservoir is established. For model solution, particle swarm optimization is applied for optimization of the new model. A heterogeneous multi-layer model was established for validation of the new method. The results obtained from the new proposed model are in consistent with the numerical simulation results. It saves a lot of computing time with the incorporation of the artificial intelligence methods. It showed that this technique is valid and effective to predict oil performance in layered reservoir. These examples showed that the application of big data and artificial intelligence method is of great significance, which not only shortens the working time, but also obtains relatively higher accuracy. Based on the objective data of the oil field and the artificial intelligence algorithm, the prediction of oil field development data can be realized. This technique has been used in nearly 100 wells of Bohai oilfields. The results showed in this paper reveals that it is possible to estimate the production performance of the water flooding reservoirs.
Abstract Drilling a directional well becomes an essential process in the oil and gas industry to ensure better reservoir exposure and less wellbore collision risk. In the high-volume drilling market, cost-effective mud motors are dominant. The motor is capable of delivering the desired well curvature by switching between rotating and sliding operations. Therefore, to follow a predefined well trajectory, it is a critical mission to determine the optimal operation control sequence of the motor. In this paper, a method of training an automatic agent for motor directional drilling using the deep reinforcement learning approach is proposed. In designing the method, motor-based directional drilling is framed into the reinforcement learning with an automatic drilling system, also known as an agent, interacting with an environment (i.e., formations, wellbore geometry, equipment) through choices of controls in a sequence. The agent perceives the states such as inclination, MD, TVD at survey points and the planned trajectories from the environment, and then decides the best action of sliding or rotating to achieve the maximum total rewards. The environment is affected by the agent's actions and returns corresponding rewards to the agent. The rewards can be positive (such as drilling to target) or negative (such as offset distance to the planned trajectory, cost of drilling, and action switching). To train our agent, currently, a drilling simulator in a simulated environment is created with layered earth model and BHA directional responses in layers. Other attributes of the drilling system are assumed to be constant and handled automatically by the simulator. The planned trajectory is also provided to the agent while training. The directional-drilling agent is trained for thousands of episodes. As a result, the agent can successfully drill to target in this simulated environment through the decisions of sliding and rotating. The proposed workflow is known as the first automated directional drilling method based on deep reinforcement learning, which makes a sequence of decisions of rotating and sliding actions to follow a planned trajectory.
Yao, Bo (Shandong Key Laboratory of Oil & Gas Storage and Transportation Safety, College of Pipeline and Civil Engineering, China University of Petroleum (East China) (Corresponding author) | Chen, Jiaqi (email: firstname.lastname@example.org)) | Li, Chuanxian (Petrochina Planning and Engineering Institute, China National Petroleum Corporation) | Yang, Fei (Shandong Key Laboratory of Oil & Gas Storage and Transportation Safety, College of Pipeline and Civil Engineering, China University of Petroleum (East China)) | Sun, Guangyu (Shandong Key Laboratory of Oil & Gas Storage and Transportation Safety, College of Pipeline and Civil Engineering, China University of Petroleum (East China)) | Lu, Yingda (Shandong Key Laboratory of Oil & Gas Storage and Transportation Safety, College of Pipeline and Civil Engineering, China University of Petroleum (East China))
Summary Accurately predicting wax deposits in a crude pipeline through empirical formulas or numerical modeling is unreliable because of the incomplete mechanism and the time-dependent unsteady actual operating conditions. With the help of the data collected by the supervisory control and data acquisition system of pipelines, wax deposit prediction is made possible by developing the time-dependent data mining method. In this article, the data from a typical long-distance crude pipeline in China operating over a 4-year time period was investigated. The inlet temperature prediction was first conducted by developing the long short-term memory (LSTM)-recurrent neural networks (RNNs) model, during which the feature sequencing, overfitting problems, and optimal hyperparameters were fully considered. Because of the time sequence cell, the accuracy of the LSTM-RNN model, as well as the time consumption, is much better than the RNN model when dealing with a great deal of data over a long period of time. Taking the inlet temperature prediction results as input features, the prediction model of average wax deposit thickness was established based on the backpropagation (BP) neural network and optimized by the particle swarm optimization (PSO), chaos particle swarm optimization (CPSO), and adaptive chaos particle swarm optimization (ACPSO) algorithms. The conclusions and associated algorithm from this article help to determine the reasonable pigging circle of longdistance pipelines practically. It could also be applied to guide the wax deposit prediction in the wellbore or oil-gathering pipes. Introduction Wax deposition has always been one of the key issues challenging the safety and economic operation of long-distance crude pipelines (White et al. 2018). Generally speaking, under the impact of flow and temperature fields, paraffin waxes may precipitate and deposit on the pipe wall coupling with the nucleation or cocrystallization of crude components such as asphaltenes or resins to form a layer of sediments called the wax deposit (Wang et al. 2017, 2019b). The wax deposit will turn thicker and stronger as wax precipitates and time passes. The wax deposit will reduce the effective diameter of pipelines, reducing the transportation quantity of crude oil and even blocking pipelines in serious cases (Guozhong and Gang 2010). To alleviate the problems discussed previously caused by thick wax deposition, the most direct and practical way is pigging regularly (Li et al. 2019b).
Han, Xiaodong (China University of Petroleum, Beijing, and China National Offshore Oil Corporation Ltd.) | Zhong, Liguo (China University of Petroleum, Beijing) | Wang, Xiang (Changzhou University) | Liu, Yigang (China National Offshore Oil Corporation Ltd.) | Wang, Hongyu (China National Offshore Oil Corporation Ltd.)
Summary The determination of optimal well locations and well controls in the horizontal-well steamflooding of heavy-oil reservoirs is a meaningful but also challenging task for the complex well types and complicated mechanisms. In this paper, a framework that combines optimization algorithms and the reservoir simulator together is proposed to solve this problem. Two typical algorithms, particle-swarm optimization (PSO) and mesh adaptive direct search (MADS), are both used to study their performance on well-placement optimization, well-control optimization, and the joint optimization of these two aspects. For the joint-optimization problem, both the simultaneous approach and sequential approach are considered. A net-present-value (NPV) formula for evaluation of the steamflooding project is proposed, and optimization runs are conducted for an offshore heavy-oil reservoir by maximizing the NPV of the horizontal steamflooding pilot. The results show that both the PSO and MADS were effective for well-placement/control optimization of the horizontal steamflooding wells. The NPVs were greatly improved throughout the optimization process. The control frequency has great influence on the optimal NPV. Intermittent steamflooding might be a better choice than continuous steamflooding. The steam-injection rate and oil-production rates need to be controlled and decreased at the latter stage for mitigating steam channeling and an ineffective steam cycle between injection and production wells. For the joint-optimization problem, the simultaneous procedure finds the best solution for a case with smaller variable numbers and the sequential procedure performs better for a case with larger variable numbers. The PSO algorithm performs better than the MADS algorithm for more complex problems with larger variable numbers in both the simultaneous and sequential procedures. The sequential procedure is worth considering in practice for problems with large scale and a limited computational budget.
Summary Polymer flooding offers the potential to recover more oil from reservoirs but requires significant investments, which necessitate a robust analysis of economic upsides and downsides. Key uncertainties in designing a polymer flood are often reservoir geology and polymer degradation. The objective of this study is to understand the impact of geological uncertainties and history matching techniques on designing the optimal strategy for, and quantifying the economic risks of, polymer flooding in a heterogeneous clastic reservoir. We applied two different history matching techniques (adjoint-based and a stochastic algorithm) to match data from a prolonged waterflood in the Watt Field, a semisynthetic reservoir that contains a wide range of geological and interpretational uncertainties. Next, sensitivity studies were carried out to identify first-order parameters that impact the net present value (NPV). These parameters were then deployed in an experimental design study using Latin hypercube sampling (LHS) to generate training runs from which a proxy model was created using polynomial regression. A particle swarm optimization (PSO) algorithm was employed to optimize the NPV for the polymer flood. The same approach was used to optimize a standard waterflood for comparison. Optimizations of the polymer flood and waterflood were performed for the history-matched model ensemble and the original ensemble. The optimal strategy to deploy the polymer flood and maximize NPV varies based on the history matching technique. The average NPV and the variance are predicted to be higher in the stochastic history matching compared to the adjoint technique. This difference is due to the ability of the stochastic algorithm to explore the parameter space more broadly, which created situations in which the oil in place is shifted upward, resulting in a higher NPV. Optimizing a history-matched ensemble leads to a narrow range in absolute NPV compared to optimizing the original ensemble. This difference is because the uncertainties associated with polymer degradation are not captured during history matching. The result of cross comparison, in which an optimal polymer design strategy for one ensemble member is deployed to the other ensemble members, predicted a decline in NPV but surprisingly still showed that the overall NPV is higher than for an optimized waterflood, even for suboptimal polymer injection strategies. This observation indicates that a polymer flood could be beneficial compared to a waterflood, even if geological uncertainties are not captured properly.
Yao, Jun (China University of Petroleum, East China (Corresponding author) | Li, Zhihao (email: email@example.com)) | Liu, Lijun (China University of Petroleum, East China) | Fan, Weipeng (China University of Petroleum, East China) | Zhang, Mingshan (China National Offshore Oil Corporation Research Institute) | Zhang, Kai (University of Alberta)
Summary Horizontal drilling and hydraulic fracturing are recognized as the most efficient techniques to enhance recovery in shale-gas reservoirs. Because of the exploitation difficulties and complex flow mechanism in shale gas, it is imperative to focus on the optimization of fracturing parameters. However, most of the current heuristic algorithms follow the principle that the variable dimension is constant during iteration, which leads to poor performance when dealing with dimension-varying problems. The optimization of fracturing parameters can be regarded as a typical dimension-varying problem when considering the difference among fracture properties such as half-length and conductivity. Thus an improved algorithm named modified variable-lengthparticle-swarm optimization (PSO) (VPSO) (MVPSO) was proposed to automatically select the optimal fracturing parameters: the number of fractures as well as the corresponding fracture properties. Then, MVPSO was verified and compared with VPSO by several benchmarks. In addition, a gas/water two-phase model considering gas-adsorption and Knudsen-diffusion effects was used to describe the shale-gas flow in matrix and fracture domains. An embedded discrete-fracture model (EDFM) was applied to model the hydraulic-fracture geometries and fractal methods were adopted to generate the fracture networks. The results indicated that MVPSO showed better performance in both convergence speed and accuracy than that of VPSO, which also provided a new perspective for the optimization of fracturing parameters. Besides, the multispindle-shapedfracture-distribution pattern reached a higher net-present-value (NPV) contrast to that of homogeneous fracture distribution. The decrease of gas price leads to smaller and more nonuniform half-lengthdistribution.
Field developers are making bold step changes to form their optimization strategies on the crests of digital transformation, using massive data analytics, machine learning, cloud computing, and data-sharing strategies for oil and gas fields in all stages of development. This trend will only grow, following the imperative to ensure the sustainability of new assets and extend the life of brownfields of any size. Current trends in field development are to study, model, and understand the time-lapse effects in those fields where optimization of costs, environmental challenges, and sustainability are key differentiators. In onshore or offshore heavy-oil developments, as well as in shale oil and gas, massive data analytics and optimization techniques revolutionize work flows in field development with great force and speed. Digital oilfield frameworks, fast-track modeling, genetic algorithms, particle-swarm optimization, ensemble-based optimization, and closed-loop reservoir management are the leading best-decision loops.