When large structures such as residential compounds or public buildings are under the influence of extremely low frequency (ELF) magnetic fields, such as the one generated by a system of railways fed by 16.67 Hz, standard methods of designing shielding structures by numerical methods usually fail. The latter can be explained by the difficulty posed in the computing process by the large aspect ratios involved due to thin layers of metal (a few millimetres or centimetres) in contrast to the large dimensions of the affected structure (several tens of meters). In some cases one has to utilize special approximations such as surface conductivity, which are not easy to handle when the designed shielding structure is clearly three -dimensional. Other alternatives such as experimentation in situ are very costly. Here, a new technique is presented of mitigating the field by using three-dimensional propagation of induced currents optimizing the field reduction factors and minimizing the cost of shielding material. The particular designing method is a hybrid of numerical simulations combined with lab experimentation using scaled models of the large structure. The method is rather cost-effective and flexible as various designs can be easily tested. Results are presented in the form of magnetic field values, at various locations in the buildings, before and after this mitigation technique is applied.

In this research two support vector machines (SVMs) and a logic function were applied to identify calcareous sections automatically in wells located in the former Barco Concession, Catatumbo Basin -Colombia. During training stages the SVMs used nuclear logs, such as neutron, photoelectric factor and gamma ray in order to differentiate calcareous from clastic sections; additionally, in this stage the fractal dimension of resistive images along with mean and variance of resistivity acquired with imaging tool (of high resolution) are employed to identify textural features of the rocks. The first SVM also employed in the training stage intervals manually interpreted of fossiliferous limestone, performed by a specialized geologist integrating core and logs information of a pilot well; during classification stage, this SVM automatically recognized intervals with fossiliferous limestone by using only logs data of any well of the field. The second SVM was also trained with nuclear logs, resistivity and fractal dimension, but in this case, with information of intervals composed of calcareous shales interbedded with limestone, recognizing automatically these rock associations during classification stage without interpretation of a geologist as input data. Finally, a logic function was applied to intervals with photoelectric factor ≥ 4 and all sections not classified by the SVMs were grouped as laminated calcareous rocks. The SVMs and logic function show accuracy of 98.76 %, 94.02 % and 94.60 % respectively in five evaluated wells and can be applied to other wells in the field that have the same dataset conditions. This methodology is dependent of the data quality and all intervals affected by poor borehole conditions should be removed in order to avoid erratic interpretations. This model must to be recalibrated in case to be applied in other fields of the basin. En esta investigación dos máquinas de vector de soporte (MVS) y una función lógica fueron aplicadas para identificar automáticamente secciones calcáreas en pozos ubicados en la antigua Concesión Barco, Cuenca de Catatumbo -Colombia. Durante etapas de entrenamiento las MVS utilizaron registros nucleares, tales como neutrón, factor fotoeléctrico y rayos gamma para diferenciar secciones calcáreas de clásticas; adicionalmente, en esta etapa la dimensión fractal de las imágenes resistivas junto con la media y varianza de la resistividad adquirida con la herramienta de imágenes (de alta resolución), son empleadas para identificar rasgos texturales de las rocas. La primera MVS también empleó durante el entrenamiento intervalos manualmente interpretados de calizas fosilíferas, realizado por un geólogo especialista integrando información de núcleo y registro de un pozo piloto; posteriormente, durante la clasificación, esta MVS automáticamente reconoció intervalos con calizas fosilíferas utilizando solamente datos de registros de cualquier pozo del campo. La segunda MVS fue entrenada con registros nucleares, resistivos y dimensión fractal, pero en este caso, también con inf...

Producing from mature oil fields in Ecuador introduces many reservoir challenges, including multizone oil production and artificial lift demands due to high water cut. Traditionally, dual-string completions with sliding sleeves and dual electrical submersible pumps (ESP) were used to access different reservoir layers and obtain back allocation with standard surface well testing. One operator adopted intelligent completions (IC) well design with flow control valves and gauges that allowed single-string configurations, reducing the number of ESPs required to address lift issues. Production is commingled downstream from the flow control valves that independently adjust production from each layer. Unlike traditional dual completions, this intelligent completion is versatile and simplifies workovers, precluding the need to pull the entire lower completion. High-frequency real-time gauge data acquired from intelligent completions is applied intermittently to analyze pressure build tests and estimate reservoir properties. The paper outlines the pilot project implemented to realize the value of the intelligent completions by monitoring production parameters (layer flow rates, water cut, and productivity index) through 24/7 surveillance and periodic optimization using a software solution. The software was connected to a real-time data source that gathered data from intelligent completions (downhole pressure and temperature gauges, and valve position sensors) and automatically calculated drawdown, fluid gradients, etc., in real time. It enabled users to study trends in real-time and historical data, and set alarms for unexpected well production variations like increasing water cut, scaling, and slugging. During the pilot project, optimization cases were performed and presented to the production team with recommendations about choke positions to achieve the highest oil production. Production engineers responded to the changes by modifying ESP frequency, changing valve choke positions, and cycling valves to optimize production and improve operational efficiency.

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Reservoir engineers operating in mature fields across the world struggle to get necessary reservoir data to make their exploitation plans more realistic. Pressure transients are the most effective way to understand the dynamic behavior of the reservoir. Loss of production and cost of acquiring data versus the benefits has always been a classical management dilemma. With the advent of digital oilfield technology, the pressure and hence the deterioration in well deliverability can be continuously and cost effectively monitored. This paper illustrates how real-time data can be used to make decisions on when to invest in pressure transient tests, and when a test is run, how to minimize the downtime. The case studies presented here are for wells on electrical submersible pumps in various types of reservoirs across Latin America.The paper briefly discusses the three pillars of digital oilfield; technology, processes and people and how they work together to achieve continuous reservoir and production optimization. Reservoir analysis for wells on electrical submersible pumps (ESP) is challenging due to the restrictions imposed by the downhole equipment. Our work presented here focuses on developing workflows and interpretation techniques for this unique environment.Having sensors downhole provides operators with an opportunity to get pressure drawdown and buildup data when the ESP starts and stops. For the wells we monitor, 10% of these unscheduled events provided much coveted reservoir information without having to stop the production intentionally. For the scheduled pressure transient events, the data acquisition rates were actively changed to ensure sufficient high quality data. Also, the length of the test was decided in real time to make sure that the test was long enough to meet the objectives but not too long to increase the cost without additional benefits. Thus with real-time technology we were able to overcome the shortcomings of traditional well testing and address the concerns of both engineers and the management. Case studies are presented where production enhancement opportunities were uncovered as a result of scheduled and unscheduled events on wells producing with ESPs. The results show that more than 70% of wells can benefit from stimulation, potentially increasing production up to 300%. To make proactive decisions and act on the recommendations generated from these production enhancement opportunities is still a challenge that needs to be addressed.For fields with large numbers of ESP wells, a time snap of reservoir properties could be periodically obtained to track changes in pressure, skin and permeability for real time optimization.