Deep-time temperature field simulation of hot dry rock: A deep learning method in both time and space dimensions

Hot dry rock (HDR) geothermal resources have great development potential and prospects, owing to their wide distribution and high thermal storage temperature. HDR resources are abundant in China, but their exploration and exploitation remain in the early stages. The Yishu fault zone in Shandong Province in eastern China is in a high-temperature geothermal zone of the Pacific Rim. Three craters and 15 hot springs are in this area, which possesses abundant geothermal resources and favourable conditions for the occurrence of HDR resources. Nevertheless, HDR resources in this area have not been studied in detail, and the index-evaluation system for HDR is still imperfect. In this paper, five index layers are selected, including: the gravity and magnetic fields, geothermal field, tectonic activity, reservoir and cover characteristics, and remotely sensed features. Thirteen index elements were also selected as evaluation factors. In combination, these index layers and index elements have established a favourable evaluation index system for HDR resources in this region, and the analytic hierarchy process has been used to determine the weight of each factor. Then, the GIS spatial analysis function is used to carry out spatial superposition analyses for each index element, and finally the distribution characteristics of HDR resources east of the Yishu fault zone were determined, with four favourable selection areas being delineated. The occurrence conditions of HDR resources are analysed in detail with evaluation indexes. It is believed that HDR resources are closely related to neotectonic movement, volcanism and rocks with high heat-generation rates. Searching for areas with geothermal anomalies is indispensable for the evaluation of HDR resources. This study provides a basis for the exploration and development of HDR resources in the region, and it can also provide a reference for similar work in other parts of the country. KEY POINTS A set of evaluation indexes to determine favourable areas for HDR resources, including gravity, geothermal field, geological structure and crustal stability, heat storage and heat cover and features on remotely sensed images, is established. East of Yishu fault zone, there is a great potential of HDR resources, with an area of 19 399 km2 of favourable and more favourable areas, accounting for approximately 38.8% of the total area. HDR resources are likely closely associated with neotectonics, igneous activity, geothermal anomalies and rocks with a high heat-generation rate such as granites.

Hot dry rock (HDR) is the most abundant geothermal resource, and is found almost everywhere at depth. The technology to extract energy from HDR for practical use has been under development at the Los Alamos National Laboratory for more than twenty years. During the 1970’s, the possibility of mining the heat from HDR by circulating water through an engineered geothermal reservoir was first demonstrated on a small scale. Between 1980 and 1986, a larger, deeper, and hotter HDR reservoir was constructed. This large reservoir was subsequently mated to a permanent surface plant. A number of flow tests of this large HDR reservoir were conducted between 1991 and 1995. The results of these tests have indicated that it should be practical to operate an HDR heat mining facility to produce power on a sustained basis. An industry-led, government cost-shared project to produce and market energy generated from HDR is currently being put in place. That project should help demonstrate that HDR reservoirs can be operated to provide energy for long periods of time at rates sufficient to be commercially viable. In the longer run, additional applications of HDR technology such as water and waste treatment, and steam generation for oil field flooding may come into widespread use.

Hot dry rock (HDR) geothermal energy, as a clean and renewable energy, has potential value in meeting the rapid demand of the social economy. Predicting the temperature distribution of a subsurface target zone is a fundamental issue for the exploration and evaluation of hot dry rock. Numerical finite–element simulation is currently the mainstream method used to study the variation in underground temperature fields. However, it has difficulty in dealing with multiple geological elements of deep and complex hot dry rock models. A Unity networking for hot dry rock temperature (HDRT–UNet) is proposed in this study that incorporates the matrix rock temperature field equation for relating the three parameters of density, specific heat capacity and thermal conductivity. According to the numerical geological structures and rock parameters of cap rocks, faults and magma intrusions, a new dataset simulated by the finite element method was created for training the HDRT–UNet. The temperature simulation results in the Gonghe basin show that the predicted temperatures within faults and granites were higher than their surrounding rocks, while a lower thermal conductivity of the cap rocks caused the temperature of overlying strata to be smaller than their surrounding temperature field. The simulation results also prove that our proposed HDRT–UNet can provide a certain evolutionary knowledge for the prediction and development of geothermal reserves.

Hot dry rock (HDR) resources are gaining increasing attention as a significant renewable resource due to their low carbon footprint and stable nature. When assessing the potential of a conventional geothermal resource, a temperature field distribution is a crucial factor. However, the available geostatistical and numerical simulations methods are often influenced by data coverage and human factors. In this study, the Convolution Block Attention Module (CBAM) and Bottleneck Architecture were integrated into UNet (CBAM-B-UNet) for simulating the geothermal temperature field. The proposed CBAM-B-UNet takes in a geological model containing parameters such as density, thermal conductivity, and specific heat capacity as input, and it simulates the temperature field by dynamically blending these multiple parameters through the neural network. The bottleneck architectures and CBAM can reduce the computational cost while ensuring accuracy in the simulation. The CBAM-B-UNet was trained using thousands of geological models with various real structures and their corresponding temperature fields. The method’s applicability was verified by employing a complex geological model of hot dry rock. In the final analysis, the simulated temperature field results are compared with the theoretical steady-state crustal ground temperature model of Gonghe Basin. The results indicated a small error between them, further validating the method's superiority. During the temperature field simulation, the thermal evolution law of a symmetrical cooling front formed by low thermal conductivity and high specific heat capacity in the center of the fault zone and on both sides of granite was revealed. The temperature gradually decreases from the center towards the edges.

An investigation on hydraulic fracturing characteristics in granite geothermal reservoir

Heat storage efficiency of wood floor made by various tree species is different. Author’s research group has developed the equipment for testing the thermal storage efficiency. The number of temperature sensors is few and it’s difficult to construct a complete and continuous temperature field in cavity of this equipment. In this paper, BP neural network is used to predict and analyze the temperature field in the closed cavity in time and space dimensions based on the temperature data of the finite points obtained from the test. In the time dimension, the coordinate and the temperatures of 3 nodes are taken as inputs and the temperature of the fourth node is taken as the output. The results are: MRE= 0.2694%, MAE= 5.9162%,MSE= 0.4225%, $R^{2}$=0.9988. In the space dimension, the temperatures of 150 measured points are used as the model training and testing sample which can chose from geometric view. The results are: MRE=.3641, MAE =4.7817, MSE=0.5216, $R^{2}$ =0.9985. It can be concluded from above: that the BP neural network used in this paper can effectively obtain the continuous and complete temperature field inside the testing cavity of the wood floor, which provides a new theoretical support for the analysis and calculation of the thermal storage efficiency of the wood floor.

As a new kind of renewable and environmental-friendly energy to generate electricity, hot dry rocks (HDR) geothermal reservoirs have been studied, along with the enhanced geothermal system (EGS). Geophysical methods have been used for the geological characterisation in different scales. The small-scale geophysical data are required for the local geological analysis so as to provide prior information for HDR modelling. Gonghe basin is in the northeast margin of the Qinghai–Tibet Plateau. Several drilling records indicate that this basin is a potential HDR prospecting area with high geothermal gradient, high heat flow and widespread igneous rock distribution, especially the Gonghe town (Qiabuqia) along with its neighbouring area. Gravity and magnetic surveys were carried out here. To better understand the areal and vertical distribution of the HDR, the gravity and magnetic data were inverted using 2D manual inversion and 3D cross-gradient joint inversion based on the smooth $${l}^{0}$$ norm constraint of minimum support functional stabiliser. The 2D model showed the sedimentary cap with a thickness of around 1000–1500 m. Granites of different periods and intrusion process were widely distributed and underlined the sediments accompanied by some deep faults. As for the HDR delineation, 3D models showed a potential area along Gonghe town and Dongba. The density and susceptibility were estimated at over 2.6 g/cm3 and 4 × 10–3 SI separately, when referred with exiting HDR distribution along the geological profile of DR4–QR1–DR3–DR2 wells. The upper boundary of HDR was outlined at the depth of around 2000 m, and the volume of HDR was then estimated around 6100 km3 above 3500 m depth. The appearance of the density and susceptibility models was affected by the lithology, stress and hydrothermal alteration. More precise geophysical methods including the microgravity, seismic and MT (magnetotelluric) surveys would be more applicable at the HDR exploitation stage.

Almost half the Australian population, aged 16-85 years, are affected by mental illness at some point in their life, and general practice plays a key role in providing effective mental health care. This paper presents the findings from a study that explored how people living with mental illness are supported in Australian general practice. A descriptive, exploratory study was conducted using semistructured interviews to gather data. The role of mental health nurses in the care and support of people with mental illness emerged from the data. This was explored further and resulted in two key themes: dimensions of time and dimensions of space. Findings from this study present key similarities and differences in the role of mental health nurses, as compared to general practitioners, in relation to dimensions of time and space. Dimensions of time and space are important considerations for general practices when planning to introduce a mental health nurse into their interprofessional team.

Hot dry rock (HDR) geothermal energy is a type of renewable energy with a great potential prospect in the deep strata. In recent years, the discovery of high-temperature HDR in Yangbajing further promotes it to be a strategic base of new geothermal resources. However, the composition, emplacement age, temperature, pressure, depth and spatiotemporal distribution of HDR are not clear, which restricts the exploration and exploitation of geothermal energy and the understanding of tectonic-thermal evolution history in this area. In this paper, a series of heat evolution processes including stratum heating up, cap-blocking heat conduction and stratum reaching stable state after being attacked by magma, is simulated. In addition, the temperature and stress field changes in HDR mining system are predicted and a series of numerical simulations are carried out. In this work, the finite element analysis method is used to simulate the conduction process of dry hot rock, mainly depending on density, Young's modulus, heat conduction coefficient, heat capacity coefficient and other parameters to establish different strata, while the boundary conditions of stress field, seepage field and temperature field are set up. The influence of fractures on HDR mining is also simulated. The purpose of this paper is to predict a series of factors affecting heat conduction of hot dry rock by numerical simulation.

FORMAL PROBLEMS IMPACT OF TRANSPORTATION RESEARCH Christian Werner University of California, I. Irvine Introduction The subject of this paper is the e m p i r i c a l , inductive approach as a method of determining quantitatively the influence which a transportation s y s - tem e x e r c i s e s on its environment. P r o b l e m s r e f e r r i n g s t r i c t l y to methods of st at i s t i cal analysis have been omitted; they are sufficiently c o v e r e d in t ex t - books on this subject. F u r t h e r m o r e , a specific form of impact, namely, the feedback on the t r a n s p o r t a t i o n s y s t e m , has not been dealt with. Since some of the m o r e important r e s e a r c h p r o b l e m s have not been t r e a t e d p r o p e r l y or not at all in the impact l i t e r a t u r e , this paper contains t r i v i a l statements in places. Its r e s u l t s are e s s e n t i a l l y crude and p r e l i m i n a r y , but they do provide some f o r m a l organization and suggest improvements of the s c a r c e body of inductive impact r e s e a r c h methods so far available. Transportation impact r e s e a r c h is commonly considered as the study of the effect which transportation phenomena e x e r c i s e on other things. Hence, the raw m a t e r i a l of such a study consists of the description of transportation features and p r o c e s s e s as well as the environment with which they interact. The final goal is the description and explanation of these interactions. Both transportation and environmental phenomena can best be described with the concept of the property space to which the dimensions of space and time are added. Thus, the input information of a transportation impact study can be organized according to the dimensions of space, time and the various p r o p e r - ties (variables). The second step will then be concerned with the determination of interrelationships between transportation and environmental v a r i a b l e s in space and time. If, in addition, the impact study is to develop predictive power, the final section has to be concerned with the extrapolation of the variables and their interrelationships over the dimensions of time, space, and property scales. II. The Variables Since r e a l world phenomena usually display a large degree of complexity, the t e r m variable should be used only for their one-dimensional p r o p e r t i e s . T h e r e is no t h e o r e t i c a l way to d e t e r m i n e always in advance, which t r a n s p o r t a - tion v a r i a b l e s e x e r c i s e influence on their environment, and which en v i r o n m en - tal v a r i a b l e s will be affected. The selection of v a r i a b l e s will t h e r e f o r e be 1The support of the Department of Transportation, F e d e r a l Railroad Administration (Contract Nr. 7-35524), Washington, D. C., is gratefully acknowledged.

One of the most important tasks family sociologists have in this International Year of the Family is to theoretically clarify the location and changing process of contemporary families.Without any theoretical frame of reference, it is impossible to point out the location of contemporary families. So, when discussing contemporary families, an explicit frame of reference is crucial.As we see, the key phrase “The International Year of the Family” signifies the policy-oriented perspective (=organized by United Nations), i.e. “control” in theoretical terms. The key phrase is also composed of both dimensions of space (= “international”) and time (= “year”).Our purpose in the theme session was to locate contemporary families in the dimensions of Time, Space and Control, using fundamental frames of reference. The session successfully demonstrated the importance and exciting nature of this purpose, proving the usefulness of theories of modern society, gender and generation as frames of references.

Aiming at saving defects and cracks of dissimilar metal materials laser welding, the finite element analysis of temperature field and new welding process of stainless steel/Al, stainless steel/Cuwere developed in this work.Taking stainless steel/Al, stainless steel/Cu dissimilar materials laser welding for example, the physical model was established, and the finite element simulation of three-dimensional temperature field of laser welding was carried out based on Abaqus CAE. The results show that the simulation of temperature field provides reference for experimental process parameters, fiber laser welding is applicable to stainless steel/Cu, stainless steel/Al with lower laser welding defect.The process parameters were optimizedto achieve better welding quality and solve welding defects.The accurate prediction of temperature field distribution in dissimilar materials laser welding is great significance for practical process guidance.

Hot dry rocks, as clean and abundant sources of new energy, are crucial in the restructuring of energy. Predicting the temperature field of hot dry rocks is of great significance for trapping the target areas of hot dry rocks. How to use limited logging data to predict the temperature field within a work area is a difficulty faced in hot dry rock exploration. We propose a method to predict the hot dry rock temperature field (using seismic inversion results). The relationship between porosity and transverse wave velocity was established with petrophysical modeling. The difference in porosity calculated from the density and transverse wave velocity was incorporated in the seismic inversion results to find the thermal expansion and predict the temperature field. We applied the method to predict the temperature of hot dry rocks in the Gonghe Basin. The results showed that the temperature in the northeast work area was higher than in the southwest area at the same depth, and a depth of 150 °C of the hot dry rock reservoir was shallower. The thermal storage cover was analyzed from the geological stratigraphic data of the Gonghe Basin. The thermal storage cover in the northeastern part was thicker than in the southwestern part and had better thermal insulation, which is consistent with the prediction of the temperature field.

Recently, we established a blended-acquisition method: temporally signatured and/or modulated and spatially dispersed source array, namely S-/M-DSA, that jointly uses various signaturing and/or modulation in the time dimension and dispersed source array in the space dimension. Our S-/M-DSA method makes the blended-acquisition encoding and operations significantly simple and robust, as well as for the deblending processing. In this paper, we discuss how this method enhances the acquisition productivity, in addition to the deblending performance, both in the time and space dimension. This shows that S-/M-DSA enhances the acquisition productivity by decreasing the sweep length by a factor of the number of frequency bands in the time dimension, and decreasing the number of simultaneous shots using non-uniform, even-but-random and multi-scale sampling in the space dimension. This also reveals that S-DSA attains a best acquisition productivity whereas M-DSA attains a best deblending performance compared to conventional blending methods.

Gonghe Basin is the most critical distribution area of hot dry rock (HDR) geothermal resources in China. Geophysical results (resistivity, velocity, density, etc.) provide an indirect way to image the spatial distribution to understand its genesis, which has a multisolution problem. However, heat flow and thermal conductivity are the most direct and reliable evidence to reveal the distribution of geothermal resources. The conventional thermal conductivity and heat flow obtained based on geologic surveys and rock samples are highly accurate, but it is challenging to characterize the regional distribution characteristics. In this paper, we develop the heat transfer adaptive finite-element equation parameter inversion workflow to invert the thermal parameters and analyze the geothermal formation mechanisms in the Gonghe Basin. First, the synthetic model test verifies that our inversion process has reliable accuracy and strong antinoise ability. For the Gonghe Basin HDR geothermal example, we use high-precision aeromagnetic data to estimate the Curie depth using the improved Parker-Oldenburg method. Then, the regional conductivity thermal and temperature field can be established according to the empirical formula as the initial input model for the inversion procedure. In addition, we collect the temperature data of six HDR geothermal wells across the Gonghe-Guide Basin as a priori constraint information. Combined with the inverted thermal parameters (thermal conductivity, geothermal gradient, and heat flow), geologic structure, and geophysical result, we infer that the high thermal conductivity layer at a depth of 5–10 km provides the main deep heat source channel and the shallow low thermal conductivity fine sandstone layer of Neogene provides good caprock protection for forming a high-temperature geothermal reservoir.