查看更多>>摘要：During the installation of deep-water drilling risers, the axial vibration caused by the heaving of drilling ships affects the safety and reliability of the operation. Conventional vibration analysis techniques consider the damping generated by the drag force to be uniformly distributed along with the risers, resulting in not real solutions. In this paper, an axial dynamics model of the drilling risers under the top excitation conditions is established, where the riser system is regarded as the assembly of an elastic rod and a block mass. Meanwhile, this model replaces the uniform damping with nonlinear distributed damping and considers the nonlinear concentrated damping of the block mass, which can be solved by the variable separation method and the iterative method. Consequently, the stress and displacement in the riser given by the present model are larger than that predicted by the model considering uniform damping or no damping. In addition, the sensitivity analysis of some parameters like the damping coefficient, the excitation amplitude and frequency, the lifting water depth, and the quality of the block mass was carried out. It shows that the stress in the top is the largest when the riser is lowered regardless of its self-weight, and it decreases almost linearly with the increase of water depth;; the axial displacement increases with the increase of the block mass and the lowering water depth but reduces with the increase of the dimensionless damping coefficient. Moreover, excessive excitation frequency will increase the equivalent damping, resulting in greater stress.
查看更多>>摘要：Riser will bend and deform in deep-water and ultra-deep water drilling, which may affect the dynamic characteristics of the drill-string. The analysis of drill-string dynamic characteristics is very difficult due to its super slenderness ratio. As the drill-string rotates in a curved narrow wellbore, the geometric nonlinearity and contact nonlinearity must be considered. In this study, a new method is proposed to analyze the drill-string dynamics considering the influence of riser deformation. The configuration of riser, based on different sea current velocities, is calculated to form a quasi-dynamic well trajectory combining with the fixed wellbore under mudline. In order to solve the dynamic problem of an extra-long drill-string model, the node iteration method is used to judge whether contact occurs at each node, and the Newmark method is used to calculate the spatial configuration of the drill-string at each time step. A program is successfully developed to analyze the dynamic characteristics of the drill-string in a deformed riser. The simulation results show that the deformation of the riser caused by ocean current has a great influence on the lateral vibration of the upper drill-string, and relatively little effect on the bottom-hole assembly. The contact and collision force near subsea wellhead is so large that may result in eccentric wear and leakage of the nearby riser.
查看更多>>摘要：Given that the goal of every hydrocarbon well is to maximize the amount of oil and/or gas produced, optimization of well completion and reservoir parameters is crucial for the development of an unconventional field. A good optimization tool is sensitivity analysis. Artificial Neural Networks (ANNs) are a fairly nascent but powerful technique that can be used to capture the effects of well completion and reservoir parameters on hydrocarbon production within a similar formation. This is because ANN models have a proven record of capturing underlying and complex correlations between complex dependent and explanatory variables. In this study, we propose using location, Volume of Fluid per Foot of Perforated Interval, Pounds ofProppant per Foot of Perforated Interval, Average Porosity, Average Water Saturation, and Average Permeability as input to train an ANN model that forecasts the first six months of oil production. We then opt to use the ANN model as a basis to explore the effect of various well completion and reservoir parameters (Volume of Fluid per Foot of Perforated Interval, Pounds ofProppant per Foot of Perforated Interval) on oil production. The dataset used consisted of 464 wells from the middle Bakken. 323 wells were used for model training, 69 for validation, 69 for testing, and 3 wells for sensitivity analysis. The average performance of the ANN model with the root mean squared error of 4109.31 bbl and R-squared of 0.78 suggests the workflow described in this study is a viable way to anticipate the oil production of a stimulated horizontal well. The sensitivity analysis then portrays a feasible way to infer production values, should completion and stimulation parameters change.
查看更多>>摘要：Proper selection of drilling parameters during planning phase to ensure an adequately clean well is the key to avoid costly drilling operational problems such as mechanical stuck pipe and high drillstring torque and dray resulting in not reaching the drilling target for highly deviated/ERD wells. Cuttings transport is a complex phenomenon influenced by different parameters such as flow rate, drillstring rotation, rate of penetration,ec-centricity, well configuration, drilling fluid properties and cuttings properties. The complexity of the cuttings transport process has led to the use of alternative modeling approaches. In this paper, a computational intelli-gence method, fuzzy logic (FL), is chosen to develop a model to estimate downhole cuttings concentration FL, resembling human reasoning, is capable of modeling non-linear functions of arbitrary complexity and dealing with imprecision in data. The FL model developed in the current work is based on 509 experimental observations involving 11 independent test parameters each, 25% of the collected dataset (excluding the test dataset) is used as cross-validation dataset and the rest is used as training dataset to avoid overfitting. The test dataset comprised of nine observations is used for comparing FL model estimates for downhole cuttings concentration to those made by two published empirical models which are based on dimensional analysis and critical drilling fluid velocity concept respectively. Comparison of the models on the test dataset shows that the developed FL model performs better than the two empirical models in all the three statistical measurements namely coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE).
查看更多>>摘要：As a powerful technique for quantitative production data analysis, type curves play an essential role in estimating reservoir and fracture properties. However, two-phase flow after hydraulic fracturing furthers the governing equation's nonlinearity and introduces substantial errors in type-curve analysis, which must be extended to two-phase systems. This study presents a new type-curve method to characterize hydraulic fracture (HF) attributes and dynamics by analyzing two-phase flowback data from multi-fractured horizontal wells (MFHWs) in hydrocarbon reservoirs. The proposed method is, to the best of our knowledge, the first semi-analytical type-curve approach that not only considers the two-phase flow in both fracture and matrix, but also takes variable BHP and rate conditions. Meanwhile, a new set of dimensionless groups are proposed to incorporate the complexity of the coupled two-phase flow in fracture and matrix into one unique curve, rather than families of curves, which significantly reduces the non-uniqueness issue of type-curve matching for MFHWs. Unlike other two-phase type curves, the pressure and saturation dependent parameters in the dimensionless groups are rigorously evaluated at the average properties within the HF based on the material balance equations. The accuracy of the proposed method is tested using the synthetic data generated from six numerical simulation cases for shale gas and oil reservoirs. The numerical validation confirms the unique behavior of type curves during fracture boundary dominated flow and verifies the accuracy of the type-curve analysis in the characterization of fracture properties. For field application, the proposed method is applied to two MFHWs in Marcellus shale gas and Eagle Ford shale oil. The agreement of interpreted results between the proposed method and straight-line analysis not only demonstrates the practicality in field application but also illustrates the superiority of the type-curve method as an easy-to-use technique to analyze two-phase flowback data.
查看更多>>摘要：This paper presents a non-isothermal, transient coupled reservoir/wellbore model that accounts for the Joule-Thomson (J-T), isentropic expansion, conduction and convection effects for predicting the transient temperature behavior and computing the wellbore temperature at different gauge depths. In this study, single phase fluid flow of oil or geothermal brine from a fully penetrating vertical or inclined well in an infinite-acting homogeneous single layer reservoir is modeled. The coupled simulator solves mass, momentum and energy conservation equations simultaneously for both reservoir and wellbore. We improve solutions by the functional iteration procedure that updates fluid properties based on available correlations as a function of pressure and temperature at a given time step. A comparison of the developed model with a commercial simulator is provided. To understand and identify diagnostic characteristics of temperature transients at gauge locations at the sandface and above the sandface that may arise during a well test, we examine the sensitivity of the model parameters appearing in the coupled non-isothermal reservoir/wellbore model through a synthetically generated test data sets and history matched field application. The drawdown and buildup sandface transient temperature data are obtained from the coupled model and used to interpret and analyze temperature transients. In addition to the J-T coefficient of fluid, history matching transient temperature data provides estimates for the skin zone radius and permeability when analyzed jointly with the conventional pressure test analysis (PTA). An investigation on the effect of gauge location on temperature data shows that the early-time response is influenced by the wellbore phenomena while the J-T effects are clearly identified at later times at typical gauge locations up to 100 m above the top of the producing horizon. Logarithmic derivative of temperature transients are found as a useful diagnostic tool to differentiate the wellbore phenomena from the reservoir response. It is also shown that the temperature transient is more reflective of the properties of the near wellbore region (e.g., skin zone) than the pressure transient. For this reason, analyzing temperature transients together with the pressure transients could add more value to the analysis to better examine near wellbore characteristics.
Mohammed A. AbujayyabMohamed HamoudaAshraf Aly Hassan
9页
查看更多>>摘要：A metadata analysis was conducted to analyze the impact of several operating conditions on the biological removal of chemical oxygen demand (COD) from produced water (PW). Operating conditions including temperature, salinity, oxygen availability, treatment technology, microorganism type, and treatment scale were investigated. No limiting maximum COD elimination capacity was reported in the literature, signaling the need to experiment with higher COD concentrations at reduced retention times to identify if such a limit exists. The maximum recorded COD elimination capacity of PW was achieved under low salinity and aerobic conditions in a membrane bioreactor using oil-degradation bacteria on a laboratory scale. Suspended growth exhibited maximum COD removal efficiency, and membrane bioreactors were the most studied technique and achieved the highest performance. Superior COD removal efficiency was realized under hypersaline conditions;; however, the COD removal efficiency under freshwater conditions was higher than that under hypersaline conditions.
查看更多>>摘要：Accurate estimation of unconventional reservoir parameters is of great significance to improve the development effect and prolong the life cycle of production wells. Reservoir parameter estimation based on pressure transient analysis (PTA) is a mainstream method due to its ease of use. However, the non-unique solution and human bias make the reliability of the method less than ideal. Therefore, a robust automatic interpretation method is urgently needed to alleviate these problems. In this work, we propose an automatic parameter evaluation method of unconventional reservoirs based on a combination of deep reinforcement learning (DRL) and PTA. Our key insight is to treat the PTA process, namely the pressure derivative curve matching process, as Markov decision process (MDP) and solve the optimal matching policy through DRL algorithm. Based on this idea, we trained an agent to automatically adjust the parameters of the trilinear flow model, a classic PTA model, and finally complete the pressure derivative curve matching to evaluate the unconventional oil reservoirs parameters. To make the training converge, branch deep Q-network with independent rewards strategy (IR-BDQ) was proposed to train the agent. Results show that IR-BDQ algorithm can effectively improve the convergence speed and parameter evaluation accuracy compared with other DRL algorithms. The results of 1000 curve matching tests showed that the mean average relative errors of parameters is 13.1%. In addition, comparison with the supervised learning algorithm reveals that the proposed method has the smallest variance of parameter inversion errors, indicating that the method has good robustness. Finally, the case study shows that the proposed method can effectively alleviate the non-unique solution problem, which is of great significance to improve the repeatability of parameter evaluation results in unconventional oil reservoirs.
查看更多>>摘要：We previously proposed a new strategy for dealing with hydraulic fracturing fluid (HFF) in shale gas reservoirs, known as zero flowback rate (ZFR) of HFF. We concluded that for some shale gas wells, the ZFR strategy appears to be a better option for dealing with injected HFF. However, neither the methods for implementing ZFR nor the benefits of ZFR were quantifiably studied in that work. In the end-face imbibition tests, shale samples from the Lower Silurian Longmaxi formation in the Sichuan basin of China were used. Non-oxidative fluid (5.5% potassium chloride solution) and oxidative fluid were also used in the tests (sodium hypochlorite solution and ammonium persulfate solution with different concentrations of 1.0%, 3.0%, and 5.0%). Based on laboratory observations and the principle of minimum energy, we propose that the number of cracks initiated by the imbibition process increases in direct proportion to the increase in the imbibition volume of the testing fluids. The crack numbers initiated by 5.0% sodium hypochlorite solution and 5.0% ammonium persulfate solution in the Longmaxi shale sample are 1.2 times and 1.4 times greater than those initiated by 5.5% potassium chloride solution, respectively. To achieve ZFR, oxidative HFF increases imbibition volume and thus shortens the shut-in time for a shale gas well. The optimum shut-in time for a 5.0% ammonium persulfate solution could be one-third that of the non-oxidative HFF.
查看更多>>摘要：Production prediction in petroleum industry plays a significant role in designing the strategy of the exploration and development. However, due to the complex and uncertain underground formations, it is difficult to obtain accurate forecasts. Most of the recent neural network studies on predicting the production rate in reservoirs are limited in optimizing the network models' parameters and reducing the significant computational cost during the training process. To address these issues, we develop a novel echo-state network (ESN) with attention mechanism to forecast the well performance based on production data. Firstly, in the training stage of the ESN model, we used the attention mechanism to extract the relevant features from contextual production rate;; secondly, a dimensionality reduction procedure of the reservoir is carried out;; thirdly, the generalization capability of the ESN model will be increased significantly by using the regularization constraints;; finally, the generic algorithm will be employed in the validation process to optimize the hyperparameters of network. Five cases were carried out to validate the prediction performance of the presented approach. The comparisons with existing deep learning methods and decline curve analysis (DCA) models were implemented. The obtained results indicate that our built model is significantly superior to other deep learning and DCA approaches currently available in the literature. The approach presented in this paper is not only developed to forecast the short-term well performance, but also the production rate reconstructed and predicted using the new method can be employed to estimate missing flow history.
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