Integration of time-lapse seismic data into automatic history matching.

As an alternate to automating history matching through the minimization approach, the ensemble Kalman filter (EnKF) can be used to rapidly update the estimate of model variables. One advantage of the EnKF method is that it is relatively easy to implement as there is no need to write extensive simulator-dependent code. The EnKF can be used to assimilate observed data as they become available to continuously update a set of reservoir simulation models, which makes the EnKF suitable for high-frequency input, for example, measurements from permanent sensors. Generation of an ensemble of "history-matched" models is very appropriate for conducting uncertainty analysis. In this work, a small synthetic case study is used to show that it is possible to integrate both time-lapse seismic impedance data and production data using the EnKF.Using the Bayesian framework, the identification of the most probable reservoir model in automatic history matching can be changed into a minimization problem, whose objective function includes square difference between observed data and computed data, as well as square difference between current model parameters and prior model information. Minimizing the objective function once can provide one realization of reservoir field. Due to the non-linearity of the equations describing 3-D, 3-phase flow in porous media, the minimization process is very demanding in both gradient and search direction computations. In this work, the adjoint method (Lagrange Multiplier) is used for gradient computation and the limited memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) method for search direction calculation.Production data, such as bottomhole pressure (Pwf), gas oil ratio (GOR), and water oil ratio (WOR), are widely used in automatic history matching. Although they can provide high resolution estimation around well locations, properties in regions far from wells are poorly constrained. To reduce uncertainty in estimation, seismic data can be integrated with the production data, to provide areally dense information across whole field. In this work, seismic P-wave impedance change data derived from time-lapse (4D) seismic are used. A series of rock physics models have been added to the reservoir simulator, C&barbelow; hevron L&barbelow; imited A&barbelow; pplication S&barbelow; imulation S&barbelow; ystem (CLASS), making it possible to compute seismic impedance using saturations and pressures from the simulator. A semi-synthetic case study is used to test the effectiveness of this methodology, and a real case from Bay Marchand field, Gulf of Mexico, shows its potential in an industry level application

MAPPING OF RESERVOIR PROPERTIES AND FACIES THROUGH INTEGRATION OF STATIC AND DYNAMIC DATA

Knowledge of the distribution of permeability and porosity in a reservoir is necessary for the prediction of future oil production, estimation of the location of bypassed oil, and optimization of reservoir management. But while the volume of data that can potentially provide information on reservoir architecture and fluid distributions has increased enormously in the past decade, it is not yet possible to make use of all the available data in an integrated fashion. While it is relatively easy to generate plausible reservoir models that honor static data such as core, log, and seismic data, it is far more difficult to generate plausible reservoir models that honor dynamic data such as transient pressures, saturations, and flow rates. As a result, the uncertainty in reservoir properties is higher than it could be and reservoir management can not be optimized. The goal of this project is to develop computationally efficient automatic history matching techniques for generating geologically plausible reservoir models which honor both static and dynamic data. Solution of this problem is necessary for the quantification of uncertainty in future reservoir performance predictions and for the optimization of reservoir management. Facies (defined here as regions of relatively uniform petrophysical properties) are common features of all reservoirs. Because the flow properties of the various facies can vary greatly, knowledge of the location of facies boundaries is of utmost importance for the prediction of reservoir performance and for the optimization of reservoir management. When the boundaries between facies are fairly well known, but flow properties are poorly known, the average properties for all facies can be determined using traditional techniques. Traditional history matching honors dynamic data by adjusting petrophysical properties in large areas, but in the process of adjusting the reservoir model ignores the static data and often results in implausible reservoir models. In general, boundary locations, average permeability and porosity, relative permeability curves, and local flow properties may all need to be adjusted to achieve a plausible reservoir model that honors all data. In this project, we will characterize the distribution of geologic facies as an indicator random field, making use of the tools of geostatistics as well as the tools of inverse and probability theory for data integration

Effects of Abscisic Acid on Wheat Anther Culture System

The effects of abscisic acid (ABA) on callus iv induction and differentiation in anther culture of spring wheat cultivars 2375 and Prospect were studied. ABA stimulated callus induction in 2375 at 0.1 µM, but not in Prospect. However, higher concentrations (\u3e1 µM) inhibited callus induction in both 2375 and Prospect. Also, there were no further ABA effects on callus differentiation when transferring the induced calli to ABA-free differentiation medium. The frenquency [sic] of callus differentiation was greatly declined with increase in concentrations of ABA which was directly added to differentiation media. ABA at 1.0 µM completely inhibited plant regeneration. Most of H1 plants appeared to be similar to their parent lines, but a few (13 out of 392) Hl Prospect plants had late heading date and varied height. ABA sensitivity was expressed in the growth of shoots challenged by ABA in the root medium. The results showed 2375 is more sensitive to ABA than Prospect. This was consistent with effect of ABA on callus induction and differentiation

Together Green : Case Study

Operational and Strategic MarketingThe rise of environmental problems drives the need of harmonizing the economic development and the ecological well-being. Nowadays more and more people are aware of the human impact on the whole ecosystem, nevertheless there are still people that are not enough conscious about this. Moreover, companies strive to qualify themselves as socially responsible enterprises by combining the corporate goals with the need to protect the environment. TogetherGreen stands for a new online platform that aims at providing an efficient online CSR communication for brands while at the same time raising people’s awareness regarding the environmental causes. Using the tools of the platform, TogetherGreen develops and implements with companies eco-friendly missions that consumers can choose according to their preferences and fill in order to get a reward in exchange, such as free products, discounts or merchandising. Consumers will get their reward as soon as they complete the requirements of the mission (for example posting a picture of his/her good deed on Facebook with #brand name). The results of the whole process is a win-win situation where both brands and consumers get rewarded and TogetherGreen is an intermediary between them. According to the market analysis, France is the most profitable market where laws, regulations and technology will successfully allow the business growth. The data collection has been mainly driven by a survey spread among potential customers in order to understand their willingness to register to the platform. The results revealed positive feedbacks and they have been the starting point for targeting the market. In fact, according to consumers’ preferences, the company will select medium-large sized companies, active on social media, which communicate fun or eco-friendly lifestyles, and able to engage loyal consumers. By selecting either eco-friendly and healthy brands, or mass-market brands, the targeting strategy is undifferentiated focusing on the common customers’ need to improve the CSR image. A deep market study shows that there is no competitor whose offer perfectly match our service. Other indirect competitors, such as traditional communication agencies, focus only on brands and have traditional communication purposes, while TogetherGreen educates people to act in an eco-friendlier way and effectively communicates the brand’s image by leveraging on consumers’ actions. In order to successfully penetrate the market, the company developed, according to the budget and goals set-up, a marketing strategy based on a comprehensive communication plan by using online channels such as the most popular social media and offline channels such as flyers or trade fairs. The entire business model relies on four revenues streams including receiving fee from brands, during the first three years of the business, and additional revenues models based on sponsorship packages, data information selling and offering of additional functionalities for brands. Due to low initial set-up costs in the first year, composed of installation, operational and communication costs, and mainly covered by the initial capital, the company promises a less risky model as this web-based tool does not require huge investments upfront. The company is going to grow very quickly on a yearly basis starting from a potential profit of 54.739 € at the end of the third year. Lastly, the company is backed by a great, diverse and heterogeneous team which will ensure the scalability of the business project. Initially composed by five business experts with knowledge in management consulting, social media marketing, finance and accounting, the team is going to be enlarged over years. Moreover, the operations are going to be supported by expert external partners such as a logistic company, a graphic designer and an external accountant who will ensure the professionality of the service. Summing up all the key aspects of the business idea developed, TogetherGreen is a profitable and promising business placed in a new market place. It will be able to disrupt the traditional concept of communication agencies and at the same time it will forge environmental conscious consumers

Analyzing Comprehensive QoS with Security Constraints for Services Composition Applications in Wireless Sensor Networks

Services composition is fundamental to software development in multi-service wireless sensor networks (WSNs). The quality of service (QoS) of services composition applications (SCAs) are confronted with severe challenges due to the open, dynamic, and complex natures of WSNs. Most previous research separated various QoS indices into different fields and studied them individually due to the computational complexity. This approach ignores the mutual influence between these QoS indices, and leads to a non-comprehensive and inaccurate analysis result. The universal generating function (UGF) shows the speediness and precision in QoS analysis. However, only one QoS index at a time can be analyzed by the classic UGF. In order to efficiently analyze the comprehensive QoS of SCAs, this paper proposes an improved UGF technique—vector universal generating function (VUGF)—which considers the relationship between multiple QoS indices, including security, and can simultaneously analyze multiple QoS indices. The numerical examples demonstrate that it can be used for the evaluation of the comprehensive QoS of SCAs subjected to the security constraint in WSNs. Therefore, it can be effectively applied to the optimal design of multi-service WSNs