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The most important mechanical properties of casing and tubing are burst strength, collapse resistance and tensile strength. These properties are necessary to determine the strength of the pipe and to design a casing string. If casing is subjected to internal pressure higher than external, it is said that casing is exposed to burst pressure loading. Burst pressure loading conditions occur during well control operations, casing pressure integrity tests, pumping operations, and production operations. The MIYP of the pipe body is determined by the internal yield pressure formula found in API Bull. This equation, commonly known as the Barlow equation, calculates the internal pressure at which the tangential (or hoop) stress at the inner wall of the pipe reaches the yield strength (YS) of the material.
The oil and gas industry invests significant money and other resources in projects with highly uncertain outcomes. We drill complex wells and build gas plants, refineries, platforms, and pipelines where costly problems can occur and where associated revenues might be disappointing. We may lose our investment; we may make a handsome profit. We are in a risky business. Assessing the outcomes, assigning probabilities of occurrence and associated values, is how we analyze and prepare to manage risk.
The most striking thing about the recent symposium put on by SPE's Gulf Coast Section was the more than 100 people in the room. During the breaks, it was nearly impossible to have a conversation without a mention of how good it is to be back meeting people in person and shaking hands, like it was before the COVID-19 pandemic. Things, indeed, are looking a lot better. The feeling that day, and the message from speakers at the Acquisitions and Divestitures (A&D) Symposium, offered an upbeat outlook for those buying and selling assets and, perhaps, a path for more such gatherings. "Last year, everyone was focused on liquidity and lenders and how many people I am firing this week and cost reductions and mergers and acquisitions," said Doug Reynolds, managing director for Simmons Energy, a division of Piper Sandler.
Song, Lianteng (China National Petroleum Corporation) | Liu, Zhonghua (China National Petroleum Corporation) | Li, Chaoliu (China National Petroleum Corporation) | Ning, Congqian (China National Petroleum Corporation) | Hu, Yating (University of Electronic Science and Technology of China) | Wang, Yan (University of Electronic Science and Technology of China) | Hong, Feng (University of Electronic Science and Technology of China) | Tang, Wei (University of Electronic Science and Technology of China) | Zhuang, Yan (University of Electronic Science and Technology of China) | Zhang, Ruichang (University of Electronic Science and Technology of China) | Zhang, Yanru (University of Electronic Science and Technology of China) | Zhang, Qiong (University of Electronic Science and Technology of China)
Abstract Geomechanical properties are essential for safe drilling, successful completion, and exploration of both conventional and unconventional reservoirs, e.g. deep shale gas and shale oil. Typically, these properties could be calculated from sonic logs. However, in shale reservoirs, it is time-consuming and challenging to obtain reliable logging data due to borehole complexity and lacking of information, which often results in log deficiency and high recovery cost of incomplete datasets. In this work, we propose the bidirectional long short-term memory (BiLSTM) which is a supervised neural network algorithm that has been widely used in sequential data-based prediction to estimate geomechanical parameters. The prediction from log data can be conducted from two different aspects. 1) Single-Well prediction, the log data from a single well is divided into training data and testing data for cross validation; 2) Cross-Well prediction, a group of wells from the same geographical region are divided into training set and testing set for cross validation, as well. The logs used in this work were collected from 11 wells from Jimusaer Shale, which includes gamma ray, bulk density, resistivity, and etc. We employed 5 various machine learning algorithms for comparison, among which BiLSTM showed the best performance with an R-squared of more than 90% and an RMSE of less than 10. The predicted results can be directly used to calculate geomechanical properties, of which accuracy is also improved in contrast to conventional methods.
Pan, Jin (Wuhan University of Technology, Wuhan) | Wang, Tao (Wuhan University of Technology, Wuhan) | Xu, Ming Cai (Huazhong University of Science and Technology, Wuhan / Collaborative Innovation Centre for Advanced Ship and Deep-Sea Exploration) | Gao, Gui (Wuhan-Jiujiang Railway Passenger Transportation Hubei Co. Ltd.)
The hull block erection network process, which is performed during the master production planning stage of the shipyard, is frequently delayed because of limited resources, workspace, and block preparation ratio. In this study, a study to predict the delay with respect to the block erection schedule is conducted by considering the variability of the block preparation ratio based on the discrete event simulation algorithm. It is confirmed that the variation of the key event observance ratio is confirmed according to the variability caused by the block erection process, which has the minimum lead time in a limited resource environment, and the block preparation ratio. Furthermore, the optimal pitch value for the key event concordance is calculated based on simulation results.
Ma, Kuiqian (Tianjin Branch of CNOOC (China) Co., Ltd) | Chen, Cunliang (Tianjin Branch of CNOOC (China) Co., Ltd) | Zhang, Wei (Tianjin Branch of CNOOC (China) Co., Ltd) | Liu, Bin (Tianjin Branch of CNOOC (China) Co., Ltd) | Han, Xiaodong (CNOOC Ltd and China University of Petroleum, Beijing)
Abstract Performance prediction is one of the important contents of oilfield development. It is also an important content affecting investment decision-making, especially for offshore oilfields with large investment. At present, most oilfields in China have entered high water cut stage or even extra high water cut stage, which requires higher prediction accuracy. Water drive curve is an important method for predicting performance. Traditional methods are based on exponential formulas, but these methods have poor adaptability in high water cut period. Because traditional methods deviate from straight line in high water cut period. In this paper, a robust method for predicting performance of offshore oilfield in high water cut period based on big data and artificial intelligence is proposed. Firstly, the reasons for the "upward warping" phenomenon of traditional methods deviating from the straight line are analyzed. It is found that the main reason for the deviation is that the relationship between the relative permeability ratio of oil to water and the water saturation curve no longer conforms to the exponential relationship. So a new percolation characteristic characterization equation with stronger adaptability is proposed, which focuses on the limit of high water flooding development. On this basis, the equation of the new water drive characteristic curve is deduced theoretically, and the dynamic prediction method is established. What's more, the solution of the method is based on large data and AI algorithm. This method has been applied to many high water flooding phase permeability curves, and the coincidence rate is more than 95.6%. The new water drive characteristic curve can better reflect the percolation characteristics of high water cut reservoirs. At the same time, the performance of adjustment wells and measures on the curve of development dynamic image is analyzed. Curve warping indicates that adjustment wells or measures are effective. Field application shows that the prediction error of the new method is less than 6%, which is more in line with the needs of oilfield development. Because of the application of artificial intelligence algorithm, the application is more convenient and saves a lot of time and money. This is a process of self-learning and self-improvement. As the oil field continues over time, each actual data will be recalculated into the database. Then the fitting and correction are carried out, and then the solution is learned again. This method has been applied to several oil fields in Bohai. And the effect is remarkable, which provides a good reference for the development of other oil fields.
Wang, Jianhua (CNPC Engineering Technology R&D Company Limited) | Zhang, Jiaqi (CNPC Engineering Technology R&D Company Limited) | Yan, Lili (CNPC Engineering Technology R&D Company Limited) | Cheng, Rongchao (CNPC Engineering Technology R&D Company Limited) | Ni, Xiaoxiao (CNPC Engineering Technology R&D Company Limited) | Yang, Haijun (CNPC Engineering Technology R&D Company Limited)
Abstract Oil-based mud (OBM) is the first choice for complex deep wells due to its advantages of high-temperature resistance, good lubrication and borehole stability. But barite sagging under ultra-high temperature during the long-time stationary completion operation may lead to serious problems in ultra-deep wells, for instance, pipe sticking, density variation and well control problems. In this paper, the influence of high-temperature and high-pressure (HTHP) on the performance of oil-based completion fluid was studied, and a model of rheological parameters was established with HTHP static sag law. The barite sagging stability was evaluated by a high temperature (220°C) and high pressure (100MPa) sag instrument. The results indicated that RM6 value and static shearing force were the main factors of affecting the settlement stability. The viscosity of the completion fluid significantly decreased with the increase of temperature, but increased with the increase of pressure. In addition, the relationship was also studied between HTHP rheology and atmospheric pressure rheology at 50°C. The results showed that when RM6 value was kept above 10, the sag stability factor (SF) of oil-based completion fluid was less than 0.52 at 190°C for 10 days, which proved a good high-temperature sag stability. Furthermore, the anti-high temperature property of oil-based completion fluid was improved through enhancing the temperature-resistance of the additives. And the high-temperature-resistant organic soil was introduced to raise the RM6 value and the static shearing force. Based on these solutions, the barite sag under high temperature of the oil-based completion fluid was prevented during drilling and completion operation in ultra-high temperature wells. The oil-based completion fluid was successfully used in Well Keshen 17 (175°C,7475 m) in Kuche piedmont structure and TT 1 well (210°C,6500 m) in Sichuan basin. The casing run smoothly, the oil-test operation was completed smoothly for 15 days, and no barite sag happened. It testified that the oil-based completion fluid had excellent of high-temperature sag stability. Therefore, this oil-based completion fluid is expected to be used widely in ultra-deep wells.
Yu, Chuan (The Research Institute of Petroleum Exploration and Development Petrochina) | Yang, Qinghai (The Research Institute of Petroleum Exploration and Development Petrochina) | Wei, Songbo (The Research Institute of Petroleum Exploration and Development Petrochina) | Li, Ming (The Research Institute of Petroleum Exploration and Development Petrochina) | Fu, Tao (The Research Institute of Petroleum Exploration and Development Petrochina)
Abstract Single-layer water cut measurement is of great significance for identifying and shutting off the unwanted water, analyzing oil remained and optimizing production. Currently, however, only the water cut of multilayer mixture can be measured by testing samples taken from wellhead, a way which is widely used in oilfields. That of single-layer fluid cannot be determined yet To address the problem, this paper puts forward a new impedance sensor that offers long-term online monitoring of single-layer water cut. This sensor is based on the different electrical conductivity of oil and water. It has two layers. The inner one contains three electrodes - two at both sides sending sinusoidal excitation signals and one at the middle receiving signals that have been attenuated by the water-oil medium. With the Maxwell's model of oil-water mixed fluid, the receiver then can measure the water cut online. The outer layer of the sensor is made of PEEK, an insulative protection. In front of the electrodes lies a static mixer which makes the measurement more accurate by fully blending the two media when they flow through the electrodes. Laboratory tests are carried out with the prototype of the sensor at various oil-water mixing ratios, fluid flow rates, and temperatures. Results show that the average margin of error is within ± 3%. Higher accuracy is seen when high water cut and flow rate enable oil globules to disperse more evenly and the space in between to get wider and the RMS error is less than 2%. If the water cut drops below 80%, the aggregation of the droplets will cause wild fluctuation and more errors in the measurement. In addition, the mineralization of the mixture directly changes its conductivity, which largely impacts the result. Meanwhile, temperature can influence the ionic movement intensity and then alter the conductivity of the medium. Therefore, in practice, the sensor calibration needs to be performed according to the range of medium salinity, and the temperature of the medium is collected in real time for temperature compensation. It is shown that after the adjustment, the water cut measurement results have higher accuracy and consistency. The impedance sensor can realize online water cut monitoring for a single-layer, indicated by tests. It is more suitable for the increasing high water cut oilfields in that it is more accurate as the water cut grows.
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.