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Oil Well Testing Handbook
This is a valuable addition to any reservoir engineer's library, containing the basics of well testing methods as well as all of the latest developments in the field. Not only are \"evergreen\" subjects, such as layered reservoirs, naturally fractured reservoirs, and wellbore effects, covered in depth, but newer developments, such as well testing for horizontal wells, are covered in full chapters.*Covers real-life examples and cases.*The most up-to-date information on oil well testing available.*The perfect reference for the engineer or textbook for the petroleum engineering student.
eBook
Wavelet-Based Kalman Smoothing Method for Uncertain Parameters Processing: Applications in Oil Well-Testing Data Denoising and Prediction
The low-distortion processing of well-testing geological parameters is a key way to provide decision-making support for oil and gas field development. However, the classical processing methods face many problems, such as the stochastic nature of the data, the randomness of initial parameters, poor denoising ability, and the lack of data compression and prediction mechanisms. These problems result in poor real-time predictability of oil operation status and difficulty in offline interpreting the played back data. Given these, we propose a wavelet-based Kalman smoothing method for processing uncertain oil well-testing data. First, we use correlation and reconstruction errors as analysis indicators and determine the optimal combination of decomposition scale and vanishing moments suitable for wavelet analysis of oil data. Second, we build a ground pressure measuring platform and use the pressure gauge equipped with the optimal combination parameters to complete the downhole online wavelet decomposition, filtering, Kalman prediction, and data storage. After the storage data are played back, the optimal Kalman parameters obtained by particle swarm optimization are used to complete the data smoothing for each sample. The experiments compare the signal-to-noise ratio and the root mean square error before and after using different classical processing models. In addition, robustness analysis is added. The proposed method, on the one hand, has the features of decorrelation and compressing data, which provide technical support for real-time uploading of downhole data; on the other hand, it can perform minimal variance unbiased estimates of the data, filter out the interference and noise, reduce the reconstruction error, and make the data have a high resolution and strong robustness.
Journal Article
Structural parameters optimization for enhanced sealing performance of soluble ball seat sealing ring in shale gas volume fracturing process
In the volumetric fracturing process of tight oil and gas horizontal wells, the soluble ball seat emerges as a crucial tool for pressure sealing due to its rapid solubility, full-diameter production capacity, and straightforward construction process. This study focuses on enhancing the sealing efficacy of metal sealing rings paired with soluble ball seats. Employing finite element analysis, structural optimization, and indoor diameter reduction tests, we assess the sealing performance of metal sealing rings fabricated from traditional Al-Mg alloy (Material 1) and a modified Al-Mg/Ga alloy (Material 2). By establishing a comprehensive evaluation system for the sealing performance and utilizing finite element simulation, we compare the mechanical properties and sealing effectiveness of both materials during the setting process. Our findings reveal that Material 1’s sealing ring endures stresses surpassing allowable limits, heightening the risk of local failure. Conversely, structural optimization of the Material 2 sealing ring allows for safe installation under a standardized 5T setting force, ensuring that contact stress between the sealing ring, casing inner wall, and the sliding body exceed the fracturing fluid pressure with a relatively uniform stress distribution. Subsequent indoor diameter reduction tests verify the superior safety performance of Material 2 over Material 1, aligning with the finite element analysis outcomes. Collectively, this research delineates how finite element analysis, structural optimization, and empirical tests augment the structural safety and sealing functionality of metal sealing rings for soluble ball seats, furnishing a basis for refining the design of soluble ball seat sealing rings.
Journal Article
Prediction of Oil Reservoir Porosity Using Petrophysical Data and a New Intelligent Hybrid Method
In hydrocarbon reserves, porosity is an important parameter that defines the volume and mobility of the porous fluid. Reservoir and management operations are greatly influenced by porosity. Usually, the standard methods for determining porosity are core analysis and well testing. These methods are very expensive, and generally wells in a field do not have a core. As a result, the methods that can determine the petrophysical properties of the reservoir, including porosity and well logging charts, are very important because well logs are usually available for all wells of a field. Artificial intelligence methods are new, low-cost and accurate methods that can indirectly estimate reservoir porosity in the shortest possible time using well-logging data. In this study, a new intelligent method of support vector regression with sparrow search algorithm (SVR-SSA) was used to indirectly estimate the porosity of a hydrocarbon reservoir in southwestern Iran (Azadegan oil field). Then, the performance of the hybrid model was compared to that of support vector regression (SVR). A total of 2506 well logging data were included in the database and were divided into two categories of training data (1754 data points) and test data (752 data points) for evaluating models. For the training data set of the SVR-SSA model, R2, mean squared error (MSE), and root mean squared error (RMSE) values were 0.98, 0.000933, and 0.030555, and those for the SVR model were 0.9072, 0.001096 and 0.033108, respectively. Also, for the SVR-SSA model test data set, R2, MSE, and RMSE values were 0.9726, 0.001032, and 0.032128 and those for the SVR model were 0.8931, 0.001660 and 0.040750, respectively. Comparing SVR-SSA and SVR based on R2, MSE and RMSE performance indicators revealed that SVR-SSA outperformed other models in predicting porosity. SVR-SSA is, therefore, a powerful, fast and accurate method of indirectly estimating porosity in reservoirs where porosity is not measured directly in the core.
Journal Article
Air pollution, methane super-emitters, and oil and gas wells in Northern California: the relationship with migraine headache prevalence and exacerbation
Background
Migraine–an episodic disorder characterized by severe headache that can lead to disability–affects over 1 billion people worldwide. Prior studies have found that short-term exposure to fine particulate matter (PM
2.5
), nitrogen dioxide (NO
2
), and ozone increases risk of migraine-related emergency department (ED) visits. Our objective was to characterize the association between long-term exposure to sources of harmful emissions and common air pollutants with both migraine headache and, among patients with migraine, headache severity.
Methods
From the Sutter Health electronic health record database, we identified 89,575 prevalent migraine cases between 2014 and 2018 using a migraine probability algorithm (MPA) score and 270,564 frequency-matched controls. Sutter Health delivers care to 3.5 million patients annually in Northern California. Exposures included 2015 annual average block group-level PM
2.5
and NO
2
concentrations, inverse-distance weighted (IDW) methane emissions from 60 super-emitters located within 10 km of participant residence between 2016 and 2018, and IDW active oil and gas wells in 2015 within 10 km of each participant. We used logistic and negative binomial mixed models to evaluate the association between environmental exposures and (1) migraine case status; and (2) migraine severity (i.e., MPA score > 100, triptan prescriptions, neurology visits, urgent care migraine visits, and ED migraine visits per person-year). Models controlled for age, sex, race/ethnicity, Medicaid use, primary care visits, and block group-level population density and poverty.
Results
In adjusted analyses, for each 5 ppb increase in NO
2
, we observed 2% increased odds of migraine case status (95% CI: 1.00, 1.05) and for each 100,000 kg/hour increase in IDW methane emissions, the odds of case status also increased (OR = 1.04, 95% CI: 1.00, 1.08). We found no association between PM
2.5
or oil and gas wells and migraine case status. PM
2.5
was linearly associated with neurology visits, migraine-specific urgent care visits, and MPA score > 100, but not triptans or ED visits. NO
2
was associated with migraine-specific urgent care and ED visits, but not other severity measures. We observed limited or null associations between continuous measures of methane emissions and proximity to oil and gas wells and migraine severity.
Conclusions
Our findings illustrate the potential role of long-term exposure to multiple ambient air pollutants for prevalent migraine and migraine severity.
Journal Article
Supercritical COsub.2 Injection-Induced Fracturing in Longmaxi Shales: A Laboratory Study
Although supercritical CO[sub.2] (SC-CO[sub.2]) fracturing has shown promise in oil and gas development with demonstrated potential, its application in shale gas extraction remains in its infancy globally. In this study, fracturing experiments were conducted with water, liquid CO[sub.2] (L-CO[sub.2]), and SC-CO[sub.2], as well as SC-CO[sub.2] at varying pump rates. The results reveal that SC-CO[sub.2] fracturing produces a highly complex fracture network characterized by fractures of varying numbers, deflection angles, and tortuosity. Analysis of CO[sub.2] temperature and pressure data showed a sharp drop in injection pressure and temperature at breakdown, followed by fluctuations until injection stopped. Acoustic emission (AE) monitoring demonstrated that energy released during main fracture initiation significantly exceeded that from CO[sub.2] phase transition-driven fracture extension, underscoring the dominant role of main fractures in energy dissipation. Compared to hydraulic fracturing, SC-CO[sub.2] fracturing created a seepage area 2.2 times larger while reducing the breakdown pressure by 37.2%, indicating superior stimulation performance. These findings emphasize the potential of SC-CO[sub.2] to form intricate fracture networks, offering a promising approach for efficient shale gas extraction.
Journal Article
Effects of Curing Pressure on the Long-Term Strength Retrogression of Oil Well Cement Cured under 200 °C
The influences of curing pressure on the physical and mechanical property development of oil well cement during long-term curing were studied. Five silica-enriched cement slurries designed without and with reinforcement materials (latex fiber and nano-graphene) were autoclaved at 200 °C under two different pressures. The low pressure (50 MPa) curing was conducted for 2, 60, 90 and 180 days; the high pressure (150 MPa) curing was conducted for 2 and 360 days. The physical and mechanical properties of set cement were characterized by compressive strength, Young’s modulus, and water/gas permeability; the mineral composition and microstructure were determined by X-ray diffraction, mercury intrusion porosimetry, thermogravimetry and scanning electron microscope. Test results showed that high pressure (150 MPa) curing led to a more compact microstructure, which reduced the rate of strength retrogression in the long term. Samples with reinforcement materials, especially the latex fiber, showed higher compressive strength, Young’s modulus and lower permeability during long-term curing at both pressures.
Journal Article
Stick-Slip Vibration Suppression in Drill String Using Observer-Based LQG Controller
Hydrocarbon exploration and production activities are guaranteed through various operations including the drilling process, which is realized by using rotary drilling systems. The process involves crushing the rock by rotating the drill bit along a drill string to create a borehole. However, during this operation, violent vibrations can occur at the level of the drill string due to its random interaction with the rocks. According to their axes of occurrence, there are three types of vibrations: axial, lateral, and torsional, where the relentless status of the torsional vibrations is terminologically known as the stick-slip phenomenon. Such a phenomenon can lead to increased fatigue of the drill string and cause its abortive fracture, in addition to reducing the efficiency of the drilling process and consequently making the exploration and production operations relatively expensive. Thus, the main objective of this paper is to eliminate the severe stick-slip vibrations that appear along the drill string of the rotary drilling system according to the LQG observer-based controller approach. The rock–bit interaction term is highly nonlinear, and the bit rotational velocity is unmeasurable; an observer was first designed to estimate the unknown inputs of the model, and then the controller was implemented in the drill string model with 10 degrees of freedom. The estimation process was essentially based on surface measurements, namely, the current and rotational velocity of the top drive. Thereafter, the performance of the proposed observer-based LQG controller was tested for different simulation scenarios in a SimScape/Matlab environment, for which the controller demonstrated good robustness in suppressing the severe stick-slip vibrations. Furthermore, the simulation and experimental results were compared to other controllers designed for the same model; the proposed observer-based LQG controller showed better performance, and it was less sensitive to structured disturbances than H∞. Thence, it is highly recommended to use the proposed approach in smart rotary drilling systems.
Journal Article
Microbial Community Distribution in Low Permeability Reservoirs and Their Positive Impact on Enhanced Oil Recovery
Low permeability oil reservoirs hold an important position in the global oil resource reserves. They boast abundant reserves and serve as one of the crucial sources for crude oil reserve replacement in China and even the world. The mechanisms for improving the oil recovery rate in high-oil-bearing reservoirs include improving fluid properties, enhancing displacement efficiency, etc. However, their development is quite challenging, requiring continuous exploration and innovation in development technologies. This study addresses the unclear distribution patterns of microbial communities and the incomplete understanding of microbial enhanced oil recovery (MEOR) mechanisms in low permeability reservoirs. Utilizing high-throughput genomics and functional gene analysis techniques, combined with laboratory and field data, the study investigates the distribution and growth patterns of microbial communities in a low permeability reservoir, exemplified by the S169 block. Additionally, the potential of MEOR to enhance oil recovery and its underlying mechanisms are explored. The results indicate that microbial communities in low permeability reservoirs exhibit strong heterogeneity, with their distribution closely correlated to geological factors such as reservoir permeability and porosity. The diversity of microbial communities is positively correlated with oil recovery efficiency, and highly active microbial populations promote the production of metabolites that enhance oil recovery. The metabolic products of microorganisms help reduce the interfacial tension between oil and water, improve the fluidity of oil, and enhance the recovery rate. In addition, the structural changes in microbial communities are closely related to factors such as the permeability and porosity of reservoirs. This study provides a theoretical basis for the optimization of microbial enhanced oil recovery (MEOR) technology.
Journal Article