With the ever-increasing trend of oil production from lower pressure wells, application of artificial lift techniques is becoming inevitable. Beam pumps and electrical submersible pumps are two of the most common artificial lift methods for low and high oil production rates. But these techniques are susceptible to high gas-oil ratios, particularly at lower wellbore pressures causing gas break-out and possible gas lock. Various types of downhole separators have been recently designed upstream of the pump to resolve this issue and improve the pump efficiency. The objective of this study is to construct a state-of-art experimental facility and simulate the flow in an oil well with varying gas-oil ratios. The facility is then used to evaluate the performance of a centrifugal downhole separator. The experimental multiphase flow setup is designed, fabricated, and constructed in an efficient and automated way to simulate a typical horizontal wellbore. The well trajectory includes a 31-ft horizontal section, inclinable to ±10o, followed by a 27-ft vertical section. The casing ID is 6-in., and a 2-in. ID tubing is placed with end-of-tubing at the bottom of vertical section. The casing and tubing streams are each led to a return column, where gas and liquid flows are metered. Automated and modulated control valves are used to monitor the pressure and production from casing and tubing streams. Five Coriolis flow meters quantify density and flow rate of different fluid streams. All of the equipment is connected to a control computer via DAQ cards. The experiments are performed with air, supplied by a screw-type compressor, and water, supplied by a moyno pump. The experiments are conducted with different gas (Qg = 30-230 Mscfd) and liquid (QL = 17-700 bpd) flowrates to simulate the cases with both rod pump and ESP operations. The air-water ratio is increased for fixed water rates to identify the ranges of separator effectiveness. The tested downhole separator is an innovative design, applying gravity and centrifugal effects to perform the separation. The results indicate that average gas separation efficiency of the separator is 93% and average liquid separation efficiency is 96% over a wide range of operating conditions, as measured by return line flow meters for casing and tubing streams. The characteristics of multiphase flow in horizontal and vertical sections of the setup are observed and evaluated using surveillance cameras. The separator can be used widely in oil fields to improve gas-liquid separation and artificial lift performance. The application of pumping artificial lift methods in high GOR wells can help significantly improve the production from a wide range of volatile oil and condensate wells. This illustrates the value of utilizing innovative downhole separation strategies. This paper presents one such centrifugal downhole separator and studies its performance in enhancing the production.
Continuous power consumption from standard fuel resources is responsible for producing large-scale environmental greenhouse gases. Production of biodiesel fuels from the vegetable oils can be considered an alternative source. Effect of greenhouse gases can also be diminished. The production of biodiesel is done by a chemical process namely transesterification and usually maximized by using the Response Surface Methodology (RSM) tool. This paper presents a new approach to optimize the production of biodiesel by introducing a new variant of recently published metaheuristic Harris Hawk Optimization (HHO). The developed variant is based on the replacement of random numbers of normal distribution at the initialization phase by the random numbers generated from the Laplacian distribution. The proposed variant is named as the Laplacian Harris Hawk Optimization (LHHO) algorithm. The contribution of this paper is in twofold: firstly the performance of the proposed algorithm is verified over a well-known set of benchmark functions, and then, we applied the LHHO to maximize biodiesel production. Comparison of LHHO is carried out with five other recent metaheuristic algorithms. An optimization routine is formulated in the form of a single-objective function with a temperature, methanol to oil ratio, and catalyst concentration as the optimization variables. These parameters are optimized to maximize the production of biodiesel. The results obtained using the proposed LHHO show significant improvement as compared to other algorithms.
To analyze drilling performance a combination of Logging While Drilling data (LWD) and surface drilling data is combined. However, distance between some of the sensors, and the bit is greater than 20-30m (66-98 ft). In this case, determination of the LWD data at the bit becomes essential. This paper aims to implement machine learning algorithms to predict LWD data at the bit. The results of the model can be used to perform real-time analysis that considers the alterations in petrophysical properties, lithologies and rock strengths while drilling, without the drawbacks of LWD sensor offset. The aim of the paper is to predict LWD data at the bit by evaluating which supervised machine learning algorithm to incorporate. For training and validation of the model, a dataset of high porosity formations from multiple wells located in the North Sea has been used. Dataset included gamma ray (GR) log data recorded near the bit and drilling parameters recorded at the bit. Multi-linear regression (MLR), K-nearest neighbor (KNN) regression, random forest (RF) regression and support vector machine (SVM) regression are used for model building. The most efficient model with the best coefficient of determination (R2) is selected. The prediction forecasting for the random forest regression model was better among all the previously discussed regression models. The R2 value for the random forest regression model 98% and the KNN regression model came in second with R2 value at 95%. The worst performing regression model was the multi-linear regression model. This machine learning approach to consider the LWD sensor offset can be useful in the determination of petrophysical properties at the bit and in the real-time drilling analysis.
The focus of power producers has shifted from conventional energy sources to sustainable energy sources because of the depletion of fossil fuels and carbon emission causing global warming and climate change. Solar cells are the most prominent option to deal with these problems. The precise estimation of solar cell parameters is very much required before their installation to achieve high efficiency.In recent years applications of several optimization algorithms for parameter estimation of the solar cell have been addressed. More recently, Opposition Based Grey Wolf Optimizer (OGWO) which is an advanced version of Grey Wolf Optimizer (GWO) has been proposed. The wide applicability of this variant has been examined over some real problems. This fact motivated authors to employ this variant on parameter extraction process. The main motivation behind the implementation of OGWO on solar cell parameter estimation process is the efficiency of this version to deal with complex optimization problems. To estimate the PV cell parameter values, measurement of voltage and current are considered at three important points. Results of OGWO are compared with the results of other variants of GWO on these two models and for three films (Mono crystalline, poly crystalline and thin film). Results reveal that OGWO produces better
Fig. 1-(a) Downhole chemical addition and (b) viscosity variation with time.
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