This thesis aims to provide a reader with practical example of optimization achieved using the Lingo software. The main focus is improvement of efficiency and shortening of a production process in AKT, a company engaged in production of car parts. It is a software, which accommodates needs of the company as well as production requirements. The theoretical part is focused on detailed description of methods and procedures used in a practical part. These methods should help the reader to understand a nature of formulas relevant for the subject. Practical part focuses on the optimization using data obtained from the company AKT, evaluation of the data and subsequent consultations with a manager of production, followed by the assessment of practical relevance for the production.

Reservoir simulation is an important tool for decision making and field development management. It enables reservoir engineers to predict reservoir production performance, update an existing model to reproduce monitoring data, assess alternative field development scenarios and design robust production optimization strategies by taking into account the existing uncertainties. A big obstacle in automating model calibration and production optimization approaches is the massive computation required to predict the response of real reservoirs under proposed changes in the model inputs. To speed up reservoir response predictions without compromising accuracy, fast surrogate models have been proposed. These models are either derived by preserving the physics of the involved processes (e.g. mass balance equations) to provide reliable long-range predictions or are developed based solely on statistical relations, in which case they can only provide short-range predictions due to the absence of the physical processes that govern the long-term behavior of the reservoir. We present an alternative solution that combines the advantages of both statistics-based and physics-based methods by deriving the flow predictions in complex two-dimensional models from one-dimensional flow network models. The existing injection/production wells in the original model form the nodes or vertices of the flow network. Each pair of wells (nodes) in the flow network is connected using a one-dimensional numerical simulation model; hence, the entire reservoir is reduced to a connected network of one-dimensional simulation models where the coupling between the individual one-dimensional models is enforced at the nodes where network edges intersect. The proposed flow network model provides a useful and fast tool for characterizing inter-well connectivity, estimating drainage volume between each pair of wells, and predicting reservoir production over an extended period of time for optimization purposes. We estimate the parameters of the flow network model using a robust training approach to ensure that the flow network model reproduces the response of the original full model under a wide range of development strategies. This step helps preserve the flow network model's predictive power during the production optimization when development strategies can change at different iterations. The robust networks training and the subsequent production optimization iterations are computationally efficient as they are performed with the faster flow network model. We demonstrate the effectiveness and applicability of our proposed flow network modeling approach to rapid production optimization using two-phase waterflooding simulations in synthetic and benchmark models.

The recent success in economical production of US shales and other low permeability reservoirs is primarily due to advances in hydraulic fracturing. In this well stimulation technique, a fracturing fluid is injected into the reservoir at pressures high enough to break down the reservoir rock and form fractures. The fractures drain the hydrocarbons in the rock matrix and provide connected pathways for the transport of hydrocarbons to the wellbore. Given the low permeability of the matrix, recent studies of shale gas production suggest that nearly all of the production has to come from a ramified, well-connected network of fractures. A recent study has shown, however, that for reasons yet unknown, the production history of more than 8000 wells in the Barnett Shale can be fit with reasonable accuracy with a linear flow model based on parallel planar hydraulic fractures perpendicular to the wellbore and spaced 1-2 meters apart. The current study is carried out to provide insights into the formation and production properties of complex hydraulic fracture networks. The end goal here is optimization of hydraulic fracture treatments: creating better-connected, more productive fracture networks that can drain the reservoir more quickly. The study provides a mechanistic model of how complexity can emerge in the pattern of hydraulic fracture networks, and describes production from such networks. Invasion percolation has been used in this study to model how the pattern of hydraulic fracture networks develop. The algorithm was chosen because it allows quick testing of different “what if” scenarios while avoiding the high computation cost associated with numerical methods such as the finite element method. The rules that govern the invasion are based on a proposed geo-mechanical model of hydraulic fracture-natural fracture interactions. In the geo-mechanical model, development of fracture networks is modeled as a sequence of basic geo-mechanical events that take place as hydraulic fractures grow and interact with natural fractures. Analytical estimates are provided to predict the occurrence of each event. A complex network of connected fractures is the output of the invasion percolation algorithm and the geo-mechanical model. To predict gas production from the network, this study uses a random walk algorithm. The random walk algorithm was chosen over other numerical methods because of its advantage in handling the complex boundary conditions present in the problem, simplicity, accuracy and speed. / text

Production of electric energy is continuously affected by many factors. Therefore, tools for predicting the future production are needed. In turn, the production affects the electric energy price, which is set on electric energy exchanges. This thesis is intended to find out if the software SDDP can be used for hydrothermal power production simulations in the Nord pool area. By building a simplified model of the electric energy production in Sweden with a focus on hydro, thermal and wind power, the intention is to see how the model is affected by different conditions. The investigated conditions are several; higher and lower water inflows to the hydro power reservoirs; different amounts of installed wind power production; different price levels of emission allowances for CO2. By using the simulation software SDDP, more wind power was seen to lower the electric energy prices, as well as reduce the need of transmission of power from the northern to the southern parts of Sweden. In the simulation, Sweden was divided into four areas, connected where the main bottlenecks in the power grid are located. Water inflows to the reservoirs are crucial in the model. Actual inflow data can be bought from SMHI. However, due to the limited thesis budget, estimations were constructed instead. The estimations were difficult to make and turned out to be too high. Consequently, no reliable evaluation of the SDDP software could be done using this data.

Reservoir management, sometimes referred to as asset management in the context of petroleum reservoirs, has become recognized as an important facet of petroleum reservoir development and production operations. In the first stage of planning field development, the simulation model is calibrated to dynamic data (history matching). One of the aims of the research is to extend the streamline based generalized travel time inversion method for full field models with multimillion cells through the use of grid coarsening. This makes the streamline based inversion suitable for high resolution simulation models with decades long production history and numerous wells by significantly reducing the computational effort. In addition, a novel workflow is proposed to integrate well bottom-hole pressure data during model calibration and the approach is illustrated via application to the CO2 sequestration. In the second stage, field development strategies are optimized. The strategies are primarily focused on rate optimization followed by infill well drilling. A method is proposed to modify the streamline-based rate optimization approach which previously focused on maximizing sweep efficiency by equalizing arrival time of the waterfront to producers, to account for accelerated production for improving the net present value (NPV). Optimum compromise between maximizing sweep efficiency and maximizing NPV can be selected based on a 'trade-off curve.' The proposed method is demonstrated on field scale application considering geological uncertainty. Finally, a novel method for well placement optimization is proposed that relies on streamlines and time of flight to first locate the potential regions of poorly swept and drained oil. Specifically, the proposed approach utilizes a dynamic measure based on the total streamline time of flight combined with static and dynamic parameters to identify "Sweet-Spots" for infill drilling. The "Sweet-Spots" can be either used directly as potential well-placement locations or as starting points during application of a formal optimization technique. The main advantage of the proposed method is its computational efficiency in calculating dynamic measure map. The complete workflow was also demonstrated on a multimillion cell reservoir model of a mature carbonate field with notable success. The infill locations based on dynamic measure map have been verified by subsequent drilling.

Polymer flooding is economically successful in reservoirs where the water flood mobility ratio is high, and/or the reservoir heterogeneity is adverse, because of the improved sweep resulting from the mobility-controlled oil displacement. The performance of a polymer flood can be further improved if the process is dynamically controlled using updated reservoir models and a closed-loop production optimization scheme is implemented. However, the formulation of an optimal production strategy is based on uncertain production forecasts resulting from uncertainty in spatial representation of reservoir heterogeneity, geologic scenarios, inaccurate modeling, scaling, just to cite a few factors. Assessing the uncertainty in reservoir modeling and transferring it to uncertainty in production forecasts is crucial for efficiently controlling the process. This dissertation presents a feedback control framework that (1) assesses uncertainty in reservoir modeling and production forecasts, (2) updates the prior uncertainty in reservoir models by integrating continuously monitored production data, and (3) formulates optimal injection/production rates for the updated reservoir models. This approach focuses on assessing uncertainty in reservoir modeling and production forecasts originated mainly by uncertain geologic scenarios and spatial variations of reservoir properties (heterogeneity). This uncertainty is mapped in a metric space created by comparing multiple reservoir models and measuring differences in effective heterogeneity related to well connectivity and well responses characteristic of polymer flooding. Continuously monitored production data is used to refine the uncertainty map using a Bayesian inversion algorithm. In contrast to classical approach of history matching by model perturbation, a model selection problem is implemented where highly probable reservoir models are selected to represent the posterior uncertainty in production forecasts. The model selection procedure yields the posterior uncertainty associated with the reservoir model. The production optimization problem is solved using the posterior models and a proxy model of polymer flooding to rapidly evaluate the objective function and response surfaces to represent the relationship between well controls and an economic objective function. The value of the feedback control framework is demonstrated with two examples of polymer flooding where the economic performance was maximized. / text

Curd syneresis, a critical step in cheesemaking, directly influences the quality of cheese. The syneresis process is empirically controlled in cheese manufacturing plants. A sensor technology for this step would improve process control and enhance cheese quality. A light backscatter sensor with a Large Field of View (LFV) was tested using a central composite design over a broad range of cheese process conditions including milk pH, calcium chloride addition level, milk fat to protein ratio, temperature, and a cutting time factor (β). The research objectives were to determine if the LFV sensor could monitor coagulation and syneresis steps and provide information for predicting pressed curd moisture. Another objective was to optimize cheese yield and quality. The LFV sensor was found to monitor coagulation and syneresis and provide light backscatter information for predicting curd moisture content. A model for relating final curd moisture content with light backscatter response was developed and tested. Models for predicting whey fat losses, pressed curd moisture, and cheese yield were successfully developed (R2>0.75) using the test factors as independent variables. This was the first attempt to develop a technology for controlling pressed curd moisture using a sensor to monitor the syneresis step.

Master's thesis, made at the Institute of Production Machines, Systems and Robotics, aims, evaluate existing production of slope mowers manufactured by Dvořák – svahové sekačky s.r.o., and then perform data collection for obtaining complete technological processes of production. With the APS (Advanced Planning and Scheduling) system, simulate and design an optimized solution, with a view to significantly reduce the required storage current inventory of manufactured parts for assembly of that slope mower.

I denna uppsats har jag undersökt metoder för förvaltande av kunskap samt lärande på arbetsplatsen genom att utgå ifrån personalens expertkunskaper inom sitt arbete och hur företag kan ta tillvara dessa. Den samlade kunskapen som finns på företag är ett värdefullt verktyg. Om den skulle varit möjligt att ta tillvara den på ett effektivt sätt skulle både medarbetare och företagets utveckling kunna dra stor nytta av det. Jag har undersökt hur denna kunskap skulle kunna förvaltas för att de anställda ska kunna få mer ansvar och möjlighet att förbättra sin arbetsplats med hjälp av olika metoder. Detta har jag gjort i samarbete med företaget Jidoka Innovation och deras metoder under samlingsnamnet HelpMe. Mitt case har varit Santa Marias tortillafabrik i Vadensjö. Där har jag tillsammans med personalen utvecklat och utvärderat HelpMe:s metodik för att skapa en effektivare produktionsprocess. / In this essay I’ve researched workplace based knowledge and how companies can benefit from using it. The combined knowledge of a company work force is a powerful tool, and if you could tap that resource both the employees and the company can benefit. I have researched how this knowledge can be used in educating new employees and how this can empower the employees by giving them more control of their workplace. I advocate handing over more control of the factory to the employees. They work with the machines everyday, and often know the best way to run them, and therefore should be the first to be consulted when optimizing the workflow. I have done this thesis project with the Swedish company Jidoka Innovation AB which with their method set by the name HelpMe, specializes in workplace learning and optimization. My case the Santa Maria tortilla factory in the south of Sweden where I evaluated and developed Jidoka’s methods to see if this way of working could make a more efficient production process.

Production optimization is a recurring phenomenon, already in industrialism, production was optimized by streamlining the material flow. In 1979 Toyota came with Lean principles and that production should be optimized by streamlining the flow. The study has been carried out at Barlingbo Brewery where the production process was observed and analyzed. Forecast was made for a deeper understanding of customer demand. Then the system's assets and limitations were analyzed for purposes of identifying bottlenecks. Finally, a cyclical production plan was produced that streamlined the planning, which in turn streamlined production. / Produktionsoptimering är ett återkommande fenomen, redan vid industrialismen optimerades produktion genom att effektivisera materialflödet. 1979 kom Toyota med Leans principer där det bland annat påtalades attproduktionerborde optimeras genom att effektivisera flödet. Studienhar genomförts hos Barlingbo Bryggeri där produktionsprocessen observeradesoch analyserades. Prognos framställdes för djupare förståelse avkundernasefterfrågan, och sedan analyserades systemets tillgångar och begränsningar i syfte att identifiera flaskhalsar. Slutligen framställdes en cyklisk produktionsplan som effektiviserarplaneringen ochi sin tur effektiviserar produktionen.