A new way of reservoir management is dawning at the horizon - intelligent reservoir management utilizing continuous data from intelligent wells and/or smart fields. Even though there are many different buzz words for this new technology, they all lead to the same - managing a reservoir in REAL TIME or close to REAL TIME. Real time usually means to react to an event as it happens or within a short time lag. In the petroleum industry real time is for sure different. This "short time lag" can be hours, days or even weeks, which of course depends highly on the objective itself. Integrating real-time data into a reservoir management work flow and turn the data into value is a complex task. The bottle neck for the data flow right now is the transfer of the real time data - measured with a secondly and minutely time increment and stored on real time server - to the engineers' desktops in a clean and timely useful fashion. This paper will show ways how to provide a continuous (24/7) flow of clean data to the engineers' desktop as a first step for the intelligent reservoir management. It will be shown that the implementation of a smart field rises or falls with the ability to provide the data to the knowledge worker - the petroleum engineer. Since the data is coming into the database, let's say every hour or every other day, the engineer is not able to check this data for discrepancies. Therefore, intelligent reservoir management needs an alarm system to inform the engineers about any under performing or critical condition of a well or the reservoir itself. Another important aspect is the integration of the standard petroleum engineering tools, like Decline Curve Analysis, Material Balance, IPR curves, Reservoir Simulation, etc., into this work process. Now an Inflow Performance Relationship Curve does not only get data every other month, but every other day. This gives the engineer completely new opportunities, e.g. monitoring the permeability impairment over time. Well tests are usually a snapshot in time, but with a continuous surveillance of the reservoir parameters, the development of, e.g., the skin can be followed over time and actions can be taken in time - predictive maintenance. Neural Networks and Genetic Algorithms are other powerful tools in the real time environment, handling such a large amount of data. A Neural Network learns on the gathered data and detects their underlying relationships - the more data, the better. Afterwards, the Neural Networks can be used for predictions (predictive data mining) - for instance predicting sand production. This approach gives the engineer time to react, and prevents the equipment from harm. This work and the methodology it implies, provides a straight forward way of integrating real time data into a reservoir management process and how to gain value from the information provided by a continuous data stream. Introduction The aim of the smart field - the digital oil field of the future - is to automate as many tasks as necessary to achieve an increase in net present value of an asset. It is not only an increase in oil- or gas production, it is also a reduction of costs. For instance, a careful monitoring of the voidage ratio or the static reservoir pressure calculated with a material balance model can be used for a better allocation of the limited water volumes for injection. Many different papers and articles have been published about intelligent wells and smart fields. These papers focus mainly on the hardware aspect than how to use this data and convert them into value. De Jonge and Stundner1 showed a possible way of how to use high frequency data and turn them into economic value by using data mining technology. The big advantage of their approach is the generality so it can be implemented in any type of asset. The SPE "Real-Time Optimization" Technical Interest Group2 (TIG) gave a very good overview of how to use and gain value from high frequency measurements. Saputelli et.al.3 introduced the self-learning reservoir management. This approach is, as well, general enough to be used in any type reservoir to optimize its performance. Of course other papers are published in this area, but they deal with highly focused applications, which are not generally enough to be applied within every kind of reservoir.
In the present study the effect of an austempering heat treatment on the quasi-static and fatigue behaviour of ductile iron was studied. 40 mm Y-blocks were used for the investigations with the risers cut off before heat treatment. Testing specimens with as-cast (pearlitic) and austempered microstructure were taken out from two different positions of the Y-blocks. The austempering heat treatment increases tensile strength, yield strength, elongation at fracture and fatigue strength. Tensile and fatigue tests verify that the mechanical properties are lower close to the riser. This is explained by the varying microstructure in the Y-blocks.
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractMethods of Artificial Intelligence like Back-Propagation Neural Networks (BPNN) have become popular software tools to predict permeability and porosity from well logs during the last several years.Similar to Multiple-Linear Regression models, Back-Propagation Neural Networks are trained with a set of target values from core measurements.The Self-Organizing Map (SOM) Neural Network method applies an unsupervised training algorithm. Until now this approach has mainly been applied for clustering purposes only, not for predicting reservoir properties.In a new application, SOM technology has been merged with statistical prediction methods to derive the following types of information from well logs and core measurements in one step: 1) Synthetic lithofacies system (clustering) 2) Porosity and permeability (prediction) SOM technology also provides a data visualization tool which allows evaluating relationships between input variables (well logs) and output variables (reservoir properties).SOM models can also be combined with BPNN in order to subdivide the entire set of well log patterns into different lithofacies and run individual BPNN models for each facies.Application of this method showed increase in prediction accuracy and significant timesavings. This paper should be viewed and printed in color.
Smart Fields and Intelligent Wells are the new buzz words currently dominating in the petroleum industry. Many papers have been published so far on the implementation of intelligent wells but less has been seen on the actual application of the continuously measured data. There are many different problems the engineer is facing during his attempt to use the real time data for real time optimization. Just to mention a few of them:Data qualityMoving huge amounts of dataMany engineering software packages are not able to handle high frequency data Basically, the question is, how can the real time data stream be used for continuous asset optimization and is there any economic value to it? The first part of the SPE paper will deal with a general introduction into current problems and how they can be solved. The second part will give an actual example of the implementation of an Automated Reservoir Surveillance System1 in the Medusa field, Gulf of Mexico (GOM). Different levels of implementation have been identified:Data management and integrationAutomationReal time rate allocationDetection The first three points have been successfully implemented. The remaining point is currently in progess. A detailed view will be given on the goals which should be achieved in the end - in particular how to actually use real time data for continuous monitoring and optimization. The uniqueness of this approach lies in the continuous and automatic comparison of real values to estimated values based on computational models. If the two values deviate from each other, the engineer is notified by an alarm system. This is a powerful and new approach and can be seen as the first step towards a Smart Field implementation. Introduction Real time data can assist the engineer in managing the asset. The continuously measured data reveal more information than sporadic measurements. This helps to shorten the learning curve and to understand the behavior of the field much faster and better. In this SPE paper, we do not want to focus on the hardware aspect of intelligent wells or smart fields. We will demonstrate the integration of continuously measured data and how this process can assist the engineer with his day-to-day work. It is about Automated Reservoir Surveillance (ARS). Today, almost all deepwater offshore wells are equipped with a basic set of sensors, such as bottomhole and wellhead pressure and temperature gauges to mention only the most common ones. These sensors usually measure at frequencies ranging from seconds to minutes - depending on the requirements. The huge amount of production data generated during a month makes a manual manipulation of the data impossible. In most cases the bottom hole pressure gets extracted from the real time data historian for pressure transient analysis, but all the other measured parameters remain unused in the high frequency database. Looking into the SPE library reveals that there are many papers on how to implement an intelligent well or how to use the generated data on a very high level, but less has been said on the most important task in the middle - how to manage the real time data and bring it to the engineer. De Jonge and Stundner2 presented an approach of using data mining for automated reservoir surveillance. This is basically the theoretical background to this real life example. The Real Time Optimization Technical Interest Group3 gave a very good definition of this topic. Saputelli4 et al went one step further and introduced the Self-Learning Reservoir. This kind of field theoretically does not need human intervention - it is self-adapting.
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