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In the field of oil and gas exploration engineering, logging data is the key information for obtaining subsurface oil and gas reservoir information. Geophysicists establish accurate formation models through comprehensive logging curve data and then formulate oil and gas development strategies. However, in the actual logging process, due to the fact that instruments are often affected by multiple environmental factors, the formation resistivity change curve is shifted, making it difficult to reflect the real formation resistivity information. Especially for slim hole array lateral logging instruments, which are significantly affected by the borehole, borehole correction processing is urgently needed. To address this problem, this paper combines neural networks with the prediction of borehole correction coefficients for slim hole array lateral logging and proposes a borehole correction coefficient prediction method based on a particle swarm optimization (PSO) and backpropagation (BP) hybrid model. Firstly, this paper uses the traditional BP neural network model to predict the borehole correction coefficient. The results show that the probability that the correction coefficient error is within 5% is 92.3%. To further improve the prediction accuracy of logging curves, this paper uses the PSO-BP neural network model for training and prediction. After verification, the probability that the correction coefficient predicted by the PSO-BP model has an error within 5% is as high as 98.8%. This result indicates that the PSO-BP model has superior and stable performance and can be effectively applied in borehole correction processing. It is an efficient and reliable prediction method for slim hole correction coefficients. The research results of this paper provide strong support for realizing intelligent downhole drilling and have important practical application value.

Temperature was monitored as a function of time at several selected depth levels in a slim experimental borehole. The hole is 15 cm in diameter, 150 m deep, and effectively sealed from the influx of ground water by a plastic tube of 5 cm diameter. The mean temperature gradient is 19.2 mK/m. The borehole was drilled in 1993 and has been in equilibrium since then. The data obtained reveal that: (1) the temperature-time series showed a complex, apparently random oscillation pattern with amplitudes of up to 25 mK; (2) irregular temperature variations characterized by larger oscillations may alternate with relatively “quiet” intervals; and (3) the character of the oscillation may vary both in depth as well as in time and the transition between two distinct regimes may be sudden. The Fourier analysis detected “red noise” behavior of the signal but did not highlight any specific peak(s) corresponding to periodicity in the measured temperature series. We employed a variety of techniques (roughness coefficient, local growth of the second moment, recurrence and cross recurrence plots) to reveal the deterministic framework of the system behavior. All above methods were proven to be quite robust in the face of noise, and enabled the discovery of structures hidden in the signals produced by complex natural processes. Statistical analysis suggested the existence of a quasi-periodic intra-hole oscillatory convection. The temperature field in the hole has a dual-frequency structure, in which short period oscillations of about 10–30 minutes are superposed on longer variations of up to several hours. At certain conditions, so far not fully understood, the temperature oscillations may practically stop. The temperature remains within 1–2 mK for a period of several days when the oscillation pattern (convection ?) suddenly resumes.

Zusammenfassung Für die Auslegung von Erdwärmesondenanlagen ist die genaue Kenntnis der thermischen Eigenschaften des Untergrundes von großer Bedeutung. In der hier vorliegenden Studie wurden im Rahmen des EU-kofinanzierten Projektes „Informationsoffensive Oberflächennahe Geothermie“ am LfU Bayern an vier Kernbohrungen des Unteren und Mittleren Buntsandsteins thermische Gesteinseigenschaften bestimmt. Die Messung der Wärme- und Temperaturleitfähigkeit erfolgte mittels der Optical-Scanning-Messmethode (TCS). Die mittleren Wärmeleitfähigkeiten schwanken zwischen 2,6 ± 0,3 W / (m · K) und 3,1 ± 0,4 W / (m · K) im trockenen Zustand sowie zwischen 3,6 ± 0,3 W / (m · K) und 4,1 ± 0,6 W / (m · K) unter gesättigten Bedingungen. Die mittleren Temperaturleitfähigkeiten betragen (1,6 ± 0,2) · 10 − 6  m 2  / s für trockene und (2,0 ± 0,6) · 10 − 6  m 2  / s für gesättigte Sandsteine. Die Ergebnisse lassen regionale petrographische und lithostratigraphische Besonderheiten erkennen. Um die Übertragbarkeit der Labormessungen auf reale geothermische Systeme zu untersuchen, wurden Modellrechnungen zur Anpassungen an die Untergrundtemperatur vorgenommen. Die Resultate zeigen, dass verschiedene Ansätze von Temperaturkorrekturen zu erheblichen Differenzen in der Wärmeleitfähigkeit führen können. Die Ansätze von Somerton (Thermal properties on temperature-related behavior of rock/fluid systems, Elsevier, New York, S 257, 1992) und Sass et al. (J Geophys Res, 97:5017–5030, 1992) waren am besten für die hier untersuchten Sandsteinproben geeignet.