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The variety of available EOR techniques requires an in-depth screening to select a viable method that matches well to the reservoir rock and fluid parameters and still remains economically attractive. In the present paper a Comprehensive Integrated EOR Workflow is proposed that starts with an Advanced EOR Screening Method. This is comprised of both a Neural Network Part and an Operational Module. While the First part uses proven data mining techniques the Operational Module considers the specific features of the screened EOR Method influencing the field implementation. The Neural Network Part is based on an exhaustive review and selection of successfully deployed literature case. It uses the rock, fluid and other reservoir parameters to screen various EOR methods considering their technical-economical applicability. This Artificial Intelligence approach utilizes data mining techniques in the form of a hybrid system that makes use of a neural network as a screening tool and the genetic algorithm as an optimization tool to land into the optimum recommendation. The Operational Part enables to evaluate the implementation capability on the given field based on the specific requirements of the preselected EOR Method. The system works its way through the literature data of successful EOR projects trying to detect patterns and learning from the data the relationship between these characteristics and the feasibility of applying each EOR technique mimicking the ability of the human mind to learn from previous experience. The system is a multi-layers neural network whereby the input layer is composed of seven key reservoir parameters (depth, temperature, porosity, permeability, initial oil saturation, oil gravity and in-situ oil viscosity) while the output layer is composed of the probability of success of the evaluated EOR methods (steam, CO2 miscible, hydro-carbon miscible, in-situ combustion, polymer flooding). The number of hidden layers and neurons are optimized using genetic algorithm for best matching of the training data set and accurate prediction of the testing set. Comparing the system output with the actual applied EOR techniques in the field shows a reliable result with only a 5% miss-prediction of the total test fields. The Operational Module determines the deployment capabilities in the given reservoir considering the specific parameters of the pre-selected EOR Method, production-pressure history, Formation fluid flow properties and the actual field and well set up, thus providing an advanced EOR Screening.
The variety of available EOR techniques requires an in-depth screening to select a viable method that matches well to the reservoir rock and fluid parameters and still remains economically attractive. In the present paper a Comprehensive Integrated EOR Workflow is proposed that starts with an Advanced EOR Screening Method. This is comprised of both a Neural Network Part and an Operational Module. While the First part uses proven data mining techniques the Operational Module considers the specific features of the screened EOR Method influencing the field implementation. The Neural Network Part is based on an exhaustive review and selection of successfully deployed literature case. It uses the rock, fluid and other reservoir parameters to screen various EOR methods considering their technical-economical applicability. This Artificial Intelligence approach utilizes data mining techniques in the form of a hybrid system that makes use of a neural network as a screening tool and the genetic algorithm as an optimization tool to land into the optimum recommendation. The Operational Part enables to evaluate the implementation capability on the given field based on the specific requirements of the preselected EOR Method. The system works its way through the literature data of successful EOR projects trying to detect patterns and learning from the data the relationship between these characteristics and the feasibility of applying each EOR technique mimicking the ability of the human mind to learn from previous experience. The system is a multi-layers neural network whereby the input layer is composed of seven key reservoir parameters (depth, temperature, porosity, permeability, initial oil saturation, oil gravity and in-situ oil viscosity) while the output layer is composed of the probability of success of the evaluated EOR methods (steam, CO2 miscible, hydro-carbon miscible, in-situ combustion, polymer flooding). The number of hidden layers and neurons are optimized using genetic algorithm for best matching of the training data set and accurate prediction of the testing set. Comparing the system output with the actual applied EOR techniques in the field shows a reliable result with only a 5% miss-prediction of the total test fields. The Operational Module determines the deployment capabilities in the given reservoir considering the specific parameters of the pre-selected EOR Method, production-pressure history, Formation fluid flow properties and the actual field and well set up, thus providing an advanced EOR Screening.
The challenges in introducing new technologies or advanced innovations in the Operating Units of the E & P Companies are manifold and surprisingly it is not about a lack of ideas or missing skilled staff at all. While the mandate for any E & P company is to innovate or to lag behind, often the attempts of introducing new technologies and innovations do not succeed due to factors that were left out either at the planning or at the execution stage. On the other side in the Operating Units (OUs) often the subject is associated to an alien technology team coming from the Head Quarter offices, that is in charge of it and once they get the required data or complete the test they will be gone, thus missing the purpose of promoting and establishing an innovations friendly culture inside the organization. The smooth introduction of new technologies and innovations to overcome specific technical or operational requirement does not necessarily happen because the management has decided to do it or somebody outside the organization is pursuing that. Instead it requires following a tailor suited approach as creative processes are the result of well motivated staff rather than of a decree. Hereby an approach is proposed based on Three Key Factors and Seven Components to effectively introduce Innovations and New Technologies. The Key Factors are: The Prevailing Creative Energy Flow, The Alignment to the Company Goals, The first one is related to the dynamical interactions underlying the site/area/organization where the technology or innovation is pursuit. It is based on the fact that in every OU of an E & P Company that is delivering on a yearly production target there is a prevailing energy flow that already contains a great deal of creativity to cope with the dynamical changes of the oil and gas reservoirs and the operations. It is about recognizing this energy flow, its influencing factors, main actors, stream direction, etc. in order to be able to first get attached to it and then to steer it accordingly. The second one is about getting fully aligned to the specific company goals so that the innovation or technology is supported by all the stake holders. Further it is also about properly cascading down the company goals and to establish a mechanism that promotes effective participation of the staff, in both forms, as individuals and as a team. The last one is about being fully aware of the Main Components of the Technology and Innovation Life Cycle that beyond the technical also involves organizational, strategically, operationally and human resource aspects. The use of the proposed approach is shown in specific field case examples that document its effectiveness. Considering the specific features of the above elements will be reflected in shorter times from the idea generation to the field implementation. Moreover it enables a workflow that effectively encourages the participation of everybody in the organization in the introduction of new innovative ideas and Technologies thus supporting a continuous improvement mode and a shift to an innovations friendly organization.
Cycles of Low Oil Prices have occurred so far three times in the last two decades, and it has become evident that it is a feature of the Industry. In the low oil price scenario a condition arises whereby the break even value of the field is higher than the sales price. The traditional response recipes have been so far a resize of the activity involving layoffs and cancelling or putting projects indefinitely on hold. A closer look tough shows that while the average production cost of the barrel may be non economical, still certain oil generating activities can yield profitable oil. On the other side stream lining the process for both surface and down hole activities incorporating cost effective innovative solutions and best practices can reduce the production cost. Further looking into the capital expenses with critical eyes, performance oriented contracts, merging from purchasing to leasing, among others can result in additional savings. Likewise goal alignment to explore for creative models jointly with the Product and Service Providers provides another stream of cost optimization. This paper presents a viable alternative that allows E & Ps to refocus on cost effective measures to keep profitability or at least to minimize loses. Specific real case examples are shared. For heavy and Extra heavy Oil Fields the impact of above is emphasized due to the lowered marked price that the higher oil viscosity triggers.
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