查看更多>>摘要：Efficient hole cleaning in drilling operations is essential to ensure optimum penetration rates. This complex problem involves the simultaneous analysis of multiple parameters, including cuttings characteristics, fluid rheology, and annulus space geometry. The effect of the mud density increase due to the cuttings concentration, which itself is a function of the settling velocity and rate of penetration (ROP), must be considered for accurate calculations of the equivalent circulation density (ECD). Mechanical Specific Energy (MSE) models have been widely used in bit selection, drilling efficiency quanti-fication, drilling performance monitoring, drilling performance optimization, and ROP improvement. We attempted to employ MSE for optimized hole cleaning and controlled ECD. Cuttings concentration was integrated with the drilling MSE, which was calculated to determine the effect of different drilling parameters on hole cleaning and ECD. We proposed a new model for predicting the ECD in vertical and deviated wellbores that takes fluid and formation properties, as well as wellbore and drill string geometry and drilling operational parameters, into account. The model predicts the cuttings concentration and equivalent circulation density in vertical and deviated wells. The workflow implements the critical and settling velocity models, which aids in optimizing drilling performance and hole cleaning. The developed model was used to study the effect of different drilling parameters on ECD and help engineers optimize their operational parameters. Integrating the drilling operational parameters to provide controlling options to drillers as they monitor ECD values while maintaining safety and optimizing the drilling job is critically important.
查看更多>>摘要：In this work, a data-driven surrogate to high-fidelity numerical simulations is developed to replace the numerical simulations of porous media., This model can accurately predict flow fields for new sets of simulation runs by learning the communications among grid cells in the numerical domain. Because of the many possible random arrangements of particles and their orientation to each other, generalization of permeability with high accuracy is not trivial - nor is it practical using conventional means. Furthermore, building a comprehensive database for different grain/pore arrangements is impossible because of the cost of running numerical simulations to generate the database that represents all possible arrangements. The objective is to predict grid-level flow fields in porous media as a priori to determine the permeability of porous media. This work is a continuation of our previous research. The rationale is that once the detailed grid-level dynamics can be accurately predicted using a data-driven approach, for any configuration/topology of the porous media, the detailed dynamics could be predicted without any need for new expensive new numerical simulation runs. In this work, we improved previous work by accurately predicting permeability of the porous media, irrespective of the grain density, pore/grain shape, with a significant reduction in computational time as opposed to previous work, which was limited to a unique grain shape/size. The surrogate model is developed by employing a deep learning technique using high-fidelity numerical simulations for two-dimensional porous media consisting of circular grains, generated by varying the number and size of the circular solid grains. The robustness of the developed model is then tested for numerous variations of porous media - generated by changing the number and size of the solid grain angularity and elongation - which have not been used for developing the model. The deep convolutional neural network employed in this work combines deep U-Net and ResNet structures to capture context and enable precise localization while avoiding issues in training caused by vanishing gradients.
查看更多>>摘要：Production optimization technique, as a crucial step in the closed-loop reservoir management (CLRM), aims to achieve optimal development efficiency by adjusting development schemes (e.g., well-controls) with the aid of optimization methods. However, due to the unbearable computational burden brought by full-scale reservoir simulation, few optimizers can obtain satisfactory solution(s) within limited simulation calls, especially when the problem dimension is very high. This phenomenon is common in many real-world scenarios, which is also referred to as the "curse of dimensionality". To address this issue, a novel divide-and-conquer (DAC) optimization paradigm is proposed for production optimization problems. Specifically, given a largescale production optimization problem, it can be decomposed into a number of simpler subproblems with low dimensions. Then, to overcome the computationally expensive issue, multiple data-driven surrogates are built for the subproblems. Finally, all the subproblem surrogates are optimized cooperatively using a reuse strategy of subproblem samples. From the perspective of production optimization, the joint scheme optimization of the original problem is turned into cooperatively optimizing the schemes involved in multiple subproblems. Interestingly, the obtained subproblems always correspond to multiple flow units with weak flow interferences caused by some obstruction factors (e.g., low-permeability channel and vertical barrier layer). This indicates that the DAC method can not only serve as an optimization enhancement technique but also can be employed as an auxiliary means of connectivity analysis. In return, many connectivity analysis methods such as flow diagnostics that require fewer simulation calls can serve as the decomposition tool. More importantly, the superior flexibility of the proposed DAC-based expensive optimization framework allows it to incorporate a wide variety of state-of-the-art surrogate-assisted evolutionary solvers. In this paper, the differential evolution (DE) and two advanced surrogate-assisted evolutionary solvers are implemented under the proposed paradigm. The experimental results conducted on two 100-dimensional benchmark functions and two production optimization tasks verified the effectiveness of the proposed method.
Yahya, SolhanKee, Kok EngPuad, Mohd Jamalulhaq MohdIsmail, Mokhtar Che...
15页
查看更多>>摘要：The purpose of this research is to investigate the behaviour of steel corrosion protection in four different fluids; seawater, tap water, 10 ppg sodium chloride (NaCl) and 12 ppg potassium formate (HCOOK) which are typically used as drilling fluid in oil and gas platform. Corrosion assessment of steel exposed to simulated underbalanced drilling (UBD) wells has been examined via immersion test by weight loss technique. The experiment was carried out in high pressure-high temperature (HPHT) for 30 h in the laboratory stirred autoclave. Two commercial corrosion inhibitors were used in studying the behaviour of the surface protection. Results showed the corrosion rate was dependent on the types of fluids and inhibitors used. The order of corrosion severity or rate of steel corrosion in the individual fluids are; tap water < HCOOK < seawater < NaCl. The observation on the surface morphology through electron microscope depicted the uniform corrosion was the predominantly attack rather pitting and oxide scale formation. The scale formation on the steel surface were discussed.
查看更多>>摘要：The initial productivity of directional oil wells is essential to research during the early development stage of offshore oilfields. Since the influence factors of productivity are numerous, the nonlinear relationship among them and productivity is hard to accurately describe by the physical models in practical application. The data-driven model provides an alternative way to deal with this problem, although it neglects the physical correlation between productivity and its influence factors. Therefore, for combining the advantage of the physical model and data-driven model, the reservoir engineering theory was used to constrain the data-driven method in this study. Based on the eXtreme gradient boosting (XGBoost) trees algorithm, this paper proposed a novel physical constrained data-driven model to predict the initial productivity. The reservoir engineering theory, including the productivity formula and the monotonic correlation between productivity and the input feature, was employed as the physical constraints in this model. Moreover, the Spearman Correlation Coefficient and a modified Recursive Feature Elimination were combined to develop a feature selection method for quantitatively selecting the main influence factors of the initial productivity. Based on the production and geological data from 87 wells, the permeability, completed thickness, oil viscosity, clay content, correct factor for interference, choke size, and drawdown were selected from 19 features as the main influence factors of the initial productivity. The prediction accuracy of the model proposed in this paper is 80.18%, which is better than the previous data-driven models. Furthermore, the physical constraints have been proved to improve the forecasting accuracy of the data-driven method by 10.09%.
查看更多>>摘要：Asphaltene removal from sediments is essential for enhanced oil recovery from heavier crude oil reservoirs and tar sands and bitumen recovery from bottom products in downstream processes. Water injection or water flooding at high pressures exert shear forces that can overcome the adhesive forces between asphaltene and mineral surfaces. The adhesive forces are also affected by ions in the aqueous medium. In the current work we study asphaltene removal from silica surface using shear forces of aqueous media in a parallel plate channel. We demonstrate the effect of varying pH and surfactant conditions in aqueous media on asphaltene removal efficiency. We relate the removal efficiency with fractional asphaltene volume on the surface estimated from atomic force microscopy. The fractional asphaltene volume reduces to 0.12 at pH 10, which is approximately 50% lower than water at neutral pH at the same shear rate. We show that the water-soluble anionic surfactants are inefficient in asphaltene removal, whereas cationic surfactant reduces the asphaltene fraction to 0.30. We conclude that the removal efficiency is affected by the zeta potential of the asphaltene and the surface, where electrostatic repulsion between the asphaltene and the surface and increased wettability in the presence of cationic surfactant improves asphaltene removal.
查看更多>>摘要：Removal of ions from produced crude oil necessitates desalting with water. During desalting it is essential to remove aqueous impurities from oil to avoid corrosion and fouling in upstream oil industry as well as catalyst deactivation in downstream processing. It is also important to simultaneously minimize the environmental impact of produced wastewater by cost-effective industrially feasible techniques. Since the ion content of wastewater discharge of oil desalter unit is far from saturation, its reuse for further desalting of crude oil is proposed in this study. Biocompatible carboxymethyl cellulose (Walocel), two methyl cellulose with different methyl and 2-hydroxypropyl derivative contents (Methocel K3 and Methocel E5), and cellulose acetate, are used to promote demulsification of well water-and lagoon wastewater-in-crude oil emulsion. Screening bottle tests revealed that Walocel and Methocel E5 are more suitable. Walocel and Methocel E5 at 3000 ppm could remove 97.6% and 91.7% of well water from oil at 80 degrees C. It was observed that presence of hydrocarbon impurities and previously added demulsifier in lagoon wastewater greatly enhances initial demulsification rate for Walocel and Methocel E5 up to 5.5 and 19.6 times, respectively. Walocel and Methocel E5 at a final concentration of 3000 ppm could remove 93.5 +/- 3.6% and 95.8 +/- 4.2% of lagoon wastewater within 60 min at 80 degrees C and finally remove all oil content of the emulsion. Superior performance of Methocel E5 in complete demulsification of lagoon wastewater-in-oil is attributed to abundance of hydrogen bond forming oxygen atoms in its cellulosic backbone as well as amphiphilic 2-hydroxypropyl and hydrophobic methyl side chains which constructively interact with water molecules and hydrocarbon impurities in the lagoon wastewater. The results of this study revealed that reuse of oily wastewater in crude oil desalting may be practical and such demulsification could be fast.
查看更多>>摘要：Compositional modeling is essential when simulating any process that involves significant changes in the composition of reservoir fluids. This includes modeling the flow of multicomponent hydrocarbons in pipes, surface facilities, and subsurface rocks. However, the rigorous thermodynamics approach to obtain phase composition is computationally expensive. So, various researchers have considered using machine learning models trained with rigorous phase-equilibrium (flash) calculations to improve computational speed. Unlike previous publications that apply classical deep learning (DL) models to flash calculations, this work will demonstrate the first attempt to incorporate thermodynamics constraints into the training of these models to ensure that they honor physical laws. To this end, we generated one million different compositions with a space-filling mixture design and performed two-phase flash to obtain the corresponding phase compositions. We performed seven-fold cross-validation to ensure reliable estimates of model accuracy. We compared the physics-constrained and standard DL model results to quantify the ability of our approach to honor physical constraints. The evaluation of our physics-informed neural network (PINN) model compared to a standard DL model shows that we can incorporate physical constraints without a considerable reduction in model accuracy. Based on the test data, our model evaluation results indicate that both PINN and standard DL models achieve coefficients of determination of 97%. In contrast, the root-mean-square error of the physics-constraint errors in the PINN model is at least two times smaller than in the standard DL model. To further demonstrate that our PINN model out-performs the DL model in terms of honoring physical constraints, we generate phase envelopes using the overall compositions predicted using the PINN and DL models for several fluid mixtures in the test data. These results show the importance of incorporating the thermodynamic constraints into DL models.
查看更多>>摘要：We provide novel evidence of two different types of volatility-patterns of oil spot prices that are generated depending on which is the predominant trigger: a) spikes of volatility (which are highly erratic) are produced during periods of supply/demand crises of oil disruptions (such as the 1990/91 First-Gulf-War, 2001 US-terrorist attack, the oil conflict of Saudi-Arabia with the US in 2014/16 and with Russia in 2020-together with the Covid-19 impact-); while b) periods where economic/financial/stock market crises are the predominant trigger (such as the 1997/98 Asian and 2008/09 Global-Financial Crises and the 2017/19 oil conflicts including the 2018 stock market crisis) are associated to higher volatility persistence. Our results are very relevant since oil markets in the coming months/years are very likely to have a very high degree of uncertainty, and knowledge of the type of volatility that is generated under each of the different triggers and how it affects oil markets is very relevant for investors, speculators and policy makers.
查看更多>>摘要：Perforation is critical to create a flow pathway between a shale reservoir and the production casing for hydraulic fracturing. However, the damage characteristics of shales by abrasive waterjet (AWJ) remain unclear. To address this concern, AWJ experiment is conducted for three types of shale with different mineral compositions, including siliceous shale, calcareous shale, and carbonaceous shale. X-ray diffractometry measures the content of mineral components. A rock mechanics test system obtains the main physical and mechanical parameters of shale samples. Scanning electron microscope (SEM) and acoustic emission (AE) are used to analyze the damage characteristics. The results indicate that siliceous shale with more brittle minerals is beneficial for the AWJ to create a larger perforation effectively, while carbonaceous shale with more clay minerals significantly lowers AWJ performance. For the shale with more brittle minerals, abrasive erosion and matrix spallation govern shale failures. As the clay content increases, the primary rock damage is abrasive cutting leading to the transgranular fracture. AE signals induced by AWJ impact could reflect breaking mechanisms and identify the difference in shale lithology. The energy dissipation gradually reduces during the AWJ process. Also, the dissipation has a negative linear correlation with the content of brittle minerals. This study provides fundamental insight into understanding shale damage by AWJ impact to optimize the perforation scheme.
NSTL
Elsevier