Small-scale LNG Market Optimization – Intelligent Distribution Network

Intelligent Systems, thanks to their effectiveness and robustness, find many applications in various industries. One of such applications is optimization of distribution network of small-scale LNG market, which was highly dynamic throughout last years. LNG (Liquified Natural Gas) is a fuel produced from natural gas, but its volume is approx. 600 times smaller than in the gas (natural) state, which makes it more economically effective to transport and store. Distribution network consists of several pickup points (varying in LNG specification) and a number of destination points (varying in tanks capacities). From economic point of view, optimization of LNG truck tanks paths is an important factor in whole market development. The optimization process involves selecting a pickup point and a sequence of destination points with amount of LNG unloaded in each of them. Solution proposed in this paper is based on graph theory and advanced machine learning methods, such as reinforcement learning, recurrent neural networks and online learning. Optimization of distribution network translates directly into a number of economic benefits: reduction of LNG transport cost, shortening the delivery time, reduction of distribution costs and increase in the effectiveness of tank truck usage. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</p

Smart Optimization of Proactive Control of Petroleum Reservoir

Artificial Intelligence plays an increasingly important role in many industrial applications as it has great potential for solving complex engineering problems. One of such applications is the optimization of petroleum reservoirs production. It is crucial to produce hydrocarbons efficiently as their geological resources are limited. From an economic point of view, optimization of hydrocarbon well control is an important factor as it affects the whole market. The solution proposed in this paper is based on state-of-the-art artificial intelligence methods, optimal control, and decision tree theory. The proposed idea is to apply a novel temporal clustering algorithm utilizing an autoencoder for temporal dimensionality reduction and a temporal clustering layer for cluster assignment, to cluster wells into groups depending on the production situation that occurs in the vicinity of the well, which allows reacting proactively. Then the optimal control of wells belonging to specific groups is determined using an auto-adaptive decision tree whose parameters are optimized using a novel sequential model-based algorithm configuration method. Optimization of petroleum reservoirs production translates directly into several economic benefits: reduction in operation costs, increase in the production effectiveness and increase in overall income without any extra expenditure as only control is changed

KnAC: an approach for enhancing cluster analysis with background knowledge and explanations

Pattern discovery in multidimensional data sets has been the subject of research for decades. There exists a wide spectrum of clustering algorithms that can be used for this purpose. However, their practical applications share a common post-clustering phase, which concerns expert-based interpretation and analysis of the obtained results. We argue that this can be the bottleneck in the process, especially in cases where domain knowledge exists prior to clustering. Such a situation requires not only a proper analysis of automatically discovered clusters but also conformance checking with existing knowledge. In this work, we present Knowledge Augmented Clustering (KnAC). Its main goal is to confront expert-based labelling with automated clustering for the sake of updating and refining the former. Our solution is not restricted to any existing clustering algorithm. Instead, KnAC can serve as an augmentation of an arbitrary clustering algorithm, making the approach robust and a model-agnostic improvement of any state-of-the-art clustering method. We demonstrate the feasibility of our method on artificially, reproducible examples and in a real life use case scenario. In both cases, we achieved better results than classic clustering algorithms without augmentation.Comment: Accepted to Applied Intelligenc

Optimization Wells Placement Policy for Enhanced CO2 Storage Capacity in Mature Oil Reservoirs

One of the possibilities to reduce carbon dioxide emissions is the use of the CCS method, which consists of CO2 separation, transport and injection of carbon dioxide into geological structures such as depleted oil fields for its long-term storage. The combination of the advanced oil production method involving the injection of carbon dioxide into the reservoir (CO2-EOR) with its geological sequestration (CCS) is the CCS-EOR process. To achieve the best ecological effect, it is important to maximize the storage capacity for CO2 injected in the CCS phase. To achieve this state, it is necessary to maximize recovery factor of the reservoir during the CO2-EOR phase. For this purpose, it is important to choose the best location of CO2 injection wells. In this work, a new algorithm to optimize the location of carbon dioxide injection wells is developed. It is based on two key reservoir properties, i.e., porosity and permeability. The developed optimization procedure was tested on an exemplary oil field simulation model. The obtained results were compared with the option of arbitrary selection of injection well locations, which confirmed both the legitimacy of using well location optimization and the effectiveness of the developed optimization method

Petroleum Reservoir Control Optimization with the Use of the Auto-Adaptive Decision Trees

The global increase in energy demand and the decreasing number of newly discovered hydrocarbon reservoirs caused by the relatively low oil price means that it is crucial to exploit existing reservoirs as efficiently as possible. Optimization of the reservoir control may increase the technical and economic efficiency of the production. In this paper, a novel algorithm that automatically determines the intelligent control maximizing the NPV of a given production process was developed. The idea is to build an auto-adaptive parameterized decision tree that replaces the arbitrarily selected limit values for the selected attributes of the decision tree with parameters. To select the optimal values of the decision tree parameters, an AI-based optimization tool called SMAC (Sequential Model-based Algorithm Configuration) was used. In each iteration, the generated control sequence is introduced into the reservoir simulator to compute the NVP, which is then utilized by the SMAC tool to vary the limit values to generate a better control sequence, which leads to an improved NPV. A new tool connecting the parameterized decision tree with the reservoir simulator and the optimization tool was developed. Its application on a simulation model of a real reservoir for which the CCS-EOR process was considered allowed oil production to be increased by 3.5% during the CO2-EOR phase, reducing the amount of carbon dioxide injected at that time by 16%. Hence, the created tool allowed revenue to be increased by 49%