Prof. Elmira RAMAZANOVA, Dr. B. IBISHOV, Dr. T. RZAEV, Dr. H. MELIKOV Scientific-Research Institute “Geotechnological Problems of Oil, Gas and Chemistry” Scientific Research Institute Modern methods of pipeline condition’s neural diagnostics and registration of leakages from magistral oil-pipelines Prof. Elmira RAMAZANOVA, Dr. B. IBISHOV, Dr. T. RZAEV, Dr. H. MELIKOV

The main directions of Institute are: Scientific-Research Institute “Geotechnological Problems of Oil, Gas and Chemistry” Scientific Research Institute The main directions of Institute are: introduction of results of researches and development in a national economy and educational process; Raising the level of scientific qualification of scientists and teachers (University staff); Involving the students, post graduate students, doctors of philosophy, candidates of science and research officers in scientific investigations and design work performed by the Scientific Research Institute (SRI).

theoretic basis of non-Newton systems hydrodynamic, Scientific-Research Institute “Geotechnological Problems of Oil, Gas and Chemistry” Scientific Research Institute Fundamental science in mechanics, techniques, technology and development of oil and gas field have developed the following Basis of search, exploration, drilling of deep inclined wells with significant deflection; Methods of production and transportation of oil and gas in complicated mining and geological and geophysical conditions both in the offshore and onshore oil fields; system analysis of oil fields development and pipeline transport based on the synergetic principles. theoretic basis of non-Newton systems hydrodynamic,

Scientific-Research Institute “Geotechnological Problems of Oil, Gas and Chemistry” Scientific Research Institute Diagnosing of outflow of oil from pipelines on a basis a Artificial Neural Networks Microprocessor detection system for location of leakages of oil, oil products and gas in main ground and underwater pipelines

The general principles of construction Artificial Neural Networks

Artificial neural networks (ANN) - new area of mathematics. Scientific-Research Institute “Geotechnological Problems of Oil, Gas and Chemistry” Scientific Research Institute Artificial neural networks (ANN) - new area of mathematics. Areas of applications ANN Automation of processes of recognition Adaptive management Approximation of functional Forecasting, creation of expert systems The organization of associative memory and many other appendices With help ANN it is possible to predict, for example, parameters of the exchange market, to carry out recognition of optical or sound signals, to create the self-learning systems, capable to operate a motor vehicle at a parking or to synthesize speech under the text.

Models ANN can be in program or equipment forms Scientific-Research Institute “Geotechnological Problems of Oil, Gas and Chemistry” Scientific Research Institute Models ANN can be in program or equipment forms General characteristics of models Basis of everyone ANN make rather simple cells which simulating a work of neural in brain

Scientific-Research Institute “Geotechnological Problems of Oil, Gas and Chemistry” Scientific Research Institute Applications of some mathematical methods for diagnosing some kinds of complications (adjournment of salts, sand, paraffin, etc.) at gathering, preparation and transportation of oil. Diagnosing of outflow of oil from oil pipelines on the basis of principles artificial neural networks, being one of elements of modern information technology.

Scientific-Research Institute “Geotechnological Problems of Oil, Gas and Chemistry” Scientific Research Institute Intellectual systems, on basis ANN allows to solve a number of problems of recognition of images (objects) : Forecasting, Optimization, Diagnostics, Managements. ANN allows to solve problems, simultaneously, with technical and mathematical ways.

Scientific-Research Institute “Geotechnological Problems of Oil, Gas and Chemistry” Scientific Research Institute THE CONCLUSION For the first time was developed self-training artificial neural networks for diagnosing complications in oil and gas pipelines Developed ANN allow operatively, exactly and authentically diagnosing some kinds of difficultly found out complications. ANN is especially useful to use for diagnosing complications in underwater and underground pipelines. For application ANN is not required special researches and additional labor and material inputs . The technique is tested for sets of the data of supervision of the simple pipeline.

Microprocessor detection system for location of leakages of oil, oil products and gas in main ground and underwater pipelines

The pressure fall time difference at two pump stations will be: Δt = (tx + t1) - (tx + t2) = t1 - t2 Wave velocity from the point of damage to the first and second pump stations will be V-φ and V+φ , correspondingly. Taking into account notation decribed earlier, time of acoustic signal reaches pump stations will be: t1 = l / (V-φ); and t2 = (L - l) / (V+φ) where I is the distance from the leakange point to the upstream pump station. Then the time difference of pressure drops: Δt = t1-t2 = l / (V-φ) – (L - l) / (V+φ) From that, the distance from the upstream pump station to the leakage point will be: l = (L / 2) * ( 1 - φ / V ) + ( Δt / 2 ) * ( V – φ2/ V) From the above, if distance to the breakdown point is less than half of the distance between two pump stations (l < L/2), then time difference Δt = t1 - t2>0, if more than half of the distance (l > L/2), then the time difference is Δt = t1 - t2>0. If time difference is zero, then the breakdown locates at: l = ( L / 2 ) * ( 1 – φ / V ) Theis the fuzzi functional algorithm – i.e. the time difference Δt is calculated and then the rest of calculations performed. There are special programms for fuzzi algorithm calculations, such as fuzzi logic Detav.

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