Academic literature on the topic 'Geological mapping Oman'

ABSTRACT The Safah oil field was discovered in 1983 on the north-plunging Lekhwair Arch of northwest Oman. The arch lacks any significant structural closure and the accumulation is stratigraphically trapped within chalky high porosity-low permeability Upper Shu’aiba carbonates of mid-late Aptian age. The complexity of its trapping geometry, internal reservoir architecture, reservoir quality and hydrocarbon charge history precluded easy explanation and geological models used to describe the field evolved quite significantly over time to accommodate new data and changing regional perspectives. These had a profound influence, first upon the decision to test what was a speculative new concept exploration prospect and later during appraisal and development, in defining an optimum static reservoir model, history matching and efficient field management strategies. The original play concept developed out of a loosely constrained regional structural and stratigraphic synthesis. Early isopach mapping had identified an enormous paleohigh on the North Lekhwair Arch, which appeared well placed to receive charge in the later Cretaceous and early Tertiary. This was tilted northward during the late Miocene, when any structurally trapped oil or gas must have been spilled to the south. However, nearby analogs suggested that the northeastern margin of the Upper Shu’aiba intraplatform Bab Basin crossed the arch in the vicinity of the paleohigh and it seemed possible that remigrating hydrocarbons might have been stratigraphically trapped against the impermeable basinal facies equivalents of Shu’aiba platform carbonates. Safah-1x was drilled to test this hypothesis, just to the north of the weakly defined Upper Shu’aiba shelf break. It encountered a thin pay zone at the northern end of what proved to be a more than 1 billion barrels STIIOP accumulation. The complexity of the field became increasingly apparent during appraisal drilling. Both differentiated shelf-to-basin and layered mid-shelf ramp depositional models were proposed to describe its unexpectedly heterogeneous internal reservoir architecture. Independent petrographic, fluid property and oil isotope analyses seemingly contradicted more likely stratigraphic correlations and consensus on a static reservoir model proved difficult to reach. As a result, geologically simple layered reservoir descriptions were favored during the early development of the field. However, as the regional perspective improved with better local analogs and increasing amounts of well and seismic data, attention eventually refocused back toward a more sophisticated stratigraphic explanation. The reservoir is now interpreted to be a low-energy mid-Shu’aiba highstand composite sequence with younger lowstand shales and offlapping carbonate shoals to the south. The updip trapping mechanism is far more complex than originally anticipated, formed by discontinuities between the porous lowstand shoals. The enigmatic relationship between stratigraphic architecture and in-reservoir PVT fluid properties and d13C isotope gradients appear to reflect dual charging by a high GOR Jurassic-sourced oil during the late Cretaceous-early Tertiary and low GOR Silurian oils in the Miocene. Internal stratigraphic baffles prevented complete homogenization and the PVT and isotope gradients remain as geochemical palimpsests. This resolution of initially rather contradictory observations was achieved by synthesizing data into a coherent narrative logic, most consistent with the available geological information at all scales, from the regional and general to the local and specific. Although more advanced seismic, petrographic and geochemical technologies certainly encouraged increasingly precise interpretations, the issues they raised were still geological and so still most effectively utilized within the context of such narratives. Ultimately, it was only by assessing these against broader geological perspectives that it proved possible to judge the validity of in-field interpretations with any confidence.

Aflaj (plural of falaj) are tunnels or trenches built to deliver groundwater from its source to the point of consumption. Support vector machine (SVM) and extreme gradient boosting (XGB) machine learning models were used to predict groundwater aflaj potential in the Nizwa watershed in the Sultanate of Oman (Oman). Nizwa city is a focal point of aflaj that underlies the historical relationship between ecology, economic dynamics, agricultural systems, and human settlements. Three hyperparameter algorithms, grid search (GS), random search (RS), and Bayesian optimisation, were used to optimise the parameters of the XGB model. Sentinel-2 and light detection and ranging (LiDAR) data via geographical information systems (GIS) were employed to derive variables of land use/land cover, and hydrological, topographical, and geological factors. The groundwater aflaj potential maps were categorised into five classes: deficient, low, moderate, high, and very high. Based on the evaluation of accuracy in the training stage, the following models showed a high level of accuracy based on the area under the curve: Bayesian-XGB (0.99), GS-XGB (0.97), RS-XGB (0.96), SVM (0.96), and XGB (0.93). The validation results showed that the Bayesian hyperparameter algorithm significantly increased XGB model efficiency in modelling groundwater aflaj potential. The highest percentages of groundwater potential in the very high class were the XGB (10%), SVM (8%), GS-XGB (6%), RS-XGB (6%), and Bayesian-XGB (6%) models. Most of these areas were located in the central and northeast parts of the case study area. The study concluded that evaluating existing groundwater datasets, facilities, current, and future spatial datasets is critical in order to design systems capable of mapping groundwater aflaj based on geospatial and ML techniques. In turn, groundwater protection service projects and integrated water source management (IWSM) programs will be able to protect the aflaj irrigation system from threats by implementing timely preventative measures.

ABSTRACT We present a high-resolution structural study on the dip slope of the southern flank of Jabal Shams in the central Oman Mountains. The objectives of the study were: (1) to test existing satellite-based interpretations of structural elements in the area; (2) prepare an accurate geological map; and (3) collect an extensive structural dataset of fault and bedding planes, fault throws, veins and joints. These data are compared with existing models of tectonic evolution in the Oman Mountains and the subsurface, and used to assess the applicability of these structures as analogs for fault and fracture systems in subsurface carbonate reservoirs in Oman. The complete exposure of clean rock incised by deep wadis allowed detailed mapping of the complex fault, vein and joint system hosted by Member 3 of the Cretaceous Kahmah Group. The member was divided into eight units for mapping purposes, in about 100 m of vertical stratigraphy. The map was almost exclusively based on direct field observations. It includes measurement of fault throw in many locations and the construction of profiles, which are accurate to within a few meters. Ground-truthing of existing satellite-based interpretations of structural elements showed that faults can be mapped with high confidence using remote-sensing data. The faults range into the subseismic scale with throws as little as a few decimeters. However, the existing interpretation of lineaments as cemented fractures was shown to be incorrect: the majority of these are open fractures formed along reactivated veins. The most prominent structure in the study area is a conjugate set of ESE-striking faults with throws resolvable from several centimeters to hundreds of meters. These faults contain bundles of coarse-grained calcite veins, which may be brecciated during reactivation. We interpret these faults to be a conjugate normal- to oblique fault set, which was rotated together with bedding during the folding of the Al Jabal al-Akhdar anticline. There are many generations of calcite veins with minor offset and at high-angle-to-bedding, sometimes in en-echelon sets. Analysis of clear overprinting relationships between veins at high-angle-to-bedding is consistent with the interpretations of Holland et al. (2009a); however we interpret the anticlockwise rotation of vein strike orientation to start before and end after the normal faulting. The normal faults post-date the bedding-parallel shear veins in the study area. Thus these faults formed after the emplacement of the Semail and Hawasina Nappes. They were previously interpreted to be of the same age as the regional normal- to oblique-slip faults in the subsurface of northern Oman and the United Arab Emirates, which evolved during the early deposition of the Campanian Fiqa Formation as proposed by Filbrandt et al. (2006). We interpret them also to be coeval with the Phase I extension of Fournier et al. (2006). The reactivation of these faults and the evolution of new veins was followed by folding of the Al Jabal al-Akhdar anticline and final uplift and jointing by reactivation of pre-existing microveins. Thus the faults in the study area are of comparable kinematics and age as those in the subsurface. However they formed at much greater depth and fluid pressures, so that direct use of these structures as analogs for fault and fracture systems in subsurface reservoirs in Oman should be undertaken with care.

Djuanda Grand Forest Park (Tahura Djuanda) at Bandung, West Java Province, built-in 1985, has functioned as a conservation area concerning Indonesia’s biodiversity. It is the first Grand Forest Park among 27 of them, which was built in Indonesia. In addition, Djuanda Grand Forest Park potential for geodiversity and geoheritage concerning the geological history of the Tangkuban Parahu volcano formation since ancient times, so that conservation is needed. The Indonesian Geological Association (IAGI), in collaboration with the Djuanda Grand Forest Park Institution and the Geological Agency, researched the geoheritage potential of the Djuanda Grand Forest Park for accelerating the Tahura Djuanda to be a geoheritage area. The method used in this study consist of inventorying, identifying, analyzing, and mapping each geodiversity/geoheritage. The finding is that the Djuanda Grand Forest Park area has seven geoheritage potentials: the Dago Waterfall Lava, Pahoehoe Lava, Lalay Waterfall Lava, Omas Waterfall Lava, Ignimbrite at “Gua Belanda” and “Gua Jepang” and the Kraton Cliff Fault Scarp. By establishing the Djuanda Grand Forest Park area as a geoheritage area will expose more information about the geological history of Tangkuban Parahu Volcano’s formation through some interpretation boards (signboards) installed at each geoheritage location for conservation and education purposes through tourists visit points. In addition, West Java will be the second province to have a geoheritage after the Geoheritage of Pandeglang Regency, located in Banten Province.

Rapid urbanization rates impact significantly on the nature of Land Cover patterns of the environment, which has been evident in the depletion of vegetal reserves and in general modifying the human climatic systems (Henderson, et al., 2017; Kumar, Masago, Mishra, & Fukushi, 2018; Luo and Lau, 2017). This study explores remote sensing classification technique and other auxiliary data to determine LULCC for a period of 50 years (1967-2016). The LULCC types identified were quantitatively evaluated using the change detection approach from results of maximum likelihood classification algorithm in GIS. Accuracy assessment results were evaluated and found to be between 56 to 98 percent of the LULC classification. The change detection analysis revealed change in the LULC types in Minna from 1976 to 2016. Built-up area increases from 74.82ha in 1976 to 116.58ha in 2016. Farmlands increased from 2.23 ha to 46.45ha and bared surface increases from 120.00ha to 161.31ha between 1976 to 2016 resulting to decline in vegetation, water body, and wetlands. The Decade of rapid urbanization was found to coincide with the period of increased Public Private Partnership Agreement (PPPA). Increase in farmlands was due to the adoption of urban agriculture which has influence on food security and the environmental sustainability. The observed increase in built up areas, farmlands and bare surfaces has substantially led to reduction in vegetation and water bodies. The oscillatory nature of water bodies LULCC which was not particularly consistent with the rates of urbanization also suggests that beyond the urbanization process, other factors may influence the LULCC of water bodies in urban settlements. Keywords: Minna, Niger State, Remote Sensing, Land Surface Characteristics References Akinrinmade, A., Ibrahim, K., & Abdurrahman, A. (2012). Geological Investigation of Tagwai Dams using Remote Sensing Technique, Minna Niger State, Nigeria. Journal of Environment, 1(01), pp. 26-32. Amadi, A., & Olasehinde, P. (2010). Application of remote sensing techniques in hydrogeological mapping of parts of Bosso Area, Minna, North-Central Nigeria. International Journal of Physical Sciences, 5(9), pp. 1465-1474. Aplin, P., & Smith, G. (2008). Advances in object-based image classification. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B7), pp. 725-728. Ayele, G. T., Tebeje, A. K., Demissie, S. S., Belete, M. A., Jemberrie, M. A., Teshome, W. M., . . . Teshale, E. Z. (2018). Time Series Land Cover Mapping and Change Detection Analysis Using Geographic Information System and Remote Sensing, Northern Ethiopia. Air, Soil and Water Research, 11, p 1178622117751603. Azevedo, J. A., Chapman, L., & Muller, C. L. (2016). Quantifying the daytime and night-time urban heat island in Birmingham, UK: a comparison of satellite derived land surface temperature and high resolution air temperature observations. Remote Sensing, 8(2), p 153. Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., . . . van Coillie, F. (2014). Geographic object-based image analysis–towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, pp. 180-191. Bukata, R. P., Jerome, J. H., Kondratyev, A. S., & Pozdnyakov, D. V. (2018). Optical properties and remote sensing of inland and coastal waters: CRC press. Camps-Valls, G., Tuia, D., Bruzzone, L., & Benediktsson, J. A. (2014). Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE signal processing magazine, 31(1), pp. 45-54. Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., . . . Lu, M. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, pp. 7-27. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), pp. 171-209. Cheng, G., Han, J., Guo, L., Liu, Z., Bu, S., & Ren, J. (2015). Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images. IEEE transactions on geoscience and remote sensing, 53(8), pp. 4238-4249. Cheng, G., Han, J., Zhou, P., & Guo, L. (2014). Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS Journal of Photogrammetry and Remote Sensing, 98, pp. 119-132. Coale, A. J., & Hoover, E. M. (2015). Population growth and economic development: Princeton University Press. Congalton, R. G., & Green, K. (2008). Assessing the accuracy of remotely sensed data: principles and practices: CRC press. Corner, R. J., Dewan, A. M., & Chakma, S. (2014). Monitoring and prediction of land-use and land-cover (LULC) change Dhaka megacity (pp. 75-97): Springer. Coutts, A. M., Harris, R. J., Phan, T., Livesley, S. J., Williams, N. S., & Tapper, N. J. (2016). Thermal infrared remote sensing of urban heat: Hotspots, vegetation, and an assessment of techniques for use in urban planning. Remote Sensing of Environment, 186, pp. 637-651. Debnath, A., Debnath, J., Ahmed, I., & Pan, N. D. (2017). Change detection in Land use/cover of a hilly area by Remote Sensing and GIS technique: A study on Tropical forest hill range, Baramura, Tripura, Northeast India. International journal of geomatics and geosciences, 7(3), pp. 293-309. Desheng, L., & Xia, F. (2010). Assessing object-based classification: advantages and limitations. Remote Sensing Letters, 1(4), pp. 187-194. Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography, 29(3), pp. 390-401. Dronova, I., Gong, P., Wang, L., & Zhong, L. (2015). Mapping dynamic cover types in a large seasonally flooded wetland using extended principal component analysis and object-based classification. Remote Sensing of Environment, 158, pp. 193-206. Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118, pp. 259-272. Elmhagen, B., Destouni, G., Angerbjörn, A., Borgström, S., Boyd, E., Cousins, S., . . . Hambäck, P. (2015). Interacting effects of change in climate, human population, land use, and water use on biodiversity and ecosystem services. Ecology and Society, 20(1) Farhani, S., & Ozturk, I. (2015). Causal relationship between CO 2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environmental Science and Pollution Research, 22(20), pp. 15663-15676. Feng, L., Chen, B., Hayat, T., Alsaedi, A., & Ahmad, B. (2017). The driving force of water footprint under the rapid urbanization process: a structural decomposition analysis for Zhangye city in China. Journal of Cleaner Production, 163, pp. S322-S328. Fensham, R., & Fairfax, R. (2002). Aerial photography for assessing vegetation change: a review of applications and the relevance of findings for Australian vegetation history. Australian Journal of Botany, 50(4), pp. 415-429. Ferreira, N., Lage, M., Doraiswamy, H., Vo, H., Wilson, L., Werner, H., . . . Silva, C. (2015). Urbane: A 3d framework to support data driven decision making in urban development. Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on. Garschagen, M., & Romero-Lankao, P. (2015). Exploring the relationships between urbanization trends and climate change vulnerability. Climatic Change, 133(1), pp. 37-52. Gokturk, S. B., Sumengen, B., Vu, D., Dalal, N., Yang, D., Lin, X., . . . Torresani, L. (2015). System and method for search portions of objects in images and features thereof: Google Patents. Government, N. S. (2007). Niger state (The Power State). Retrieved from http://nigerstate.blogspot.com.ng/ Green, K., Kempka, D., & Lackey, L. (1994). Using remote sensing to detect and monitor land-cover and land-use change. Photogrammetric engineering and remote sensing, 60(3), pp. 331-337. Gu, W., Lv, Z., & Hao, M. (2017). Change detection method for remote sensing images based on an improved Markov random field. Multimedia Tools and Applications, 76(17), pp. 17719-17734. Guo, Y., & Shen, Y. (2015). Quantifying water and energy budgets and the impacts of climatic and human factors in the Haihe River Basin, China: 2. Trends and implications to water resources. Journal of Hydrology, 527, pp. 251-261. Hadi, F., Thapa, R. B., Helmi, M., Hazarika, M. K., Madawalagama, S., Deshapriya, L. N., & Center, G. (2016). Urban growth and land use/land cover modeling in Semarang, Central Java, Indonesia: Colombo-Srilanka, ACRS2016. Hagolle, O., Huc, M., Villa Pascual, D., & Dedieu, G. (2015). A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENμS and Sentinel-2 images. Remote Sensing, 7(3), pp. 2668-2691. Hegazy, I. R., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4(1), pp. 117-124. Henderson, J. V., Storeygard, A., & Deichmann, U. (2017). Has climate change driven urbanization in Africa? Journal of development economics, 124, pp. 60-82. Hu, L., & Brunsell, N. A. (2015). A new perspective to assess the urban heat island through remotely sensed atmospheric profiles. Remote Sensing of Environment, 158, pp. 393-406. Hughes, S. J., Cabral, J. A., Bastos, R., Cortes, R., Vicente, J., Eitelberg, D., . . . Santos, M. (2016). A stochastic dynamic model to assess land use change scenarios on the ecological status of fluvial water bodies under the Water Framework Directive. Science of the Total Environment, 565, pp. 427-439. Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, pp. 91-106. Hyyppä, J., Hyyppä, H., Inkinen, M., Engdahl, M., Linko, S., & Zhu, Y.-H. (2000). Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128(1-2), pp. 109-120. Jiang, L., Wu, F., Liu, Y., & Deng, X. (2014). Modeling the impacts of urbanization and industrial transformation on water resources in China: an integrated hydro-economic CGE analysis. Sustainability, 6(11), pp. 7586-7600. Jin, S., Yang, L., Zhu, Z., & Homer, C. (2017). A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011. Remote Sensing of Environment, 195, pp. 44-55. Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., . . . Mitchard, E. T. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), p 70. Kaliraj, S., Chandrasekar, N., & Magesh, N. (2015). Evaluation of multiple environmental factors for site-specific groundwater recharge structures in the Vaigai River upper basin, Tamil Nadu, India, using GIS-based weighted overlay analysis. Environmental earth sciences, 74(5), pp. 4355-4380. Koop, S. H., & van Leeuwen, C. J. (2015). Assessment of the sustainability of water resources management: A critical review of the City Blueprint approach. Water Resources Management, 29(15), pp. 5649-5670. Kumar, P., Masago, Y., Mishra, B. K., & Fukushi, K. (2018). Evaluating future stress due to combined effect of climate change and rapid urbanization for Pasig-Marikina River, Manila. Groundwater for Sustainable Development, 6, pp. 227-234. Lang, S. (2008). Object-based image analysis for remote sensing applications: modeling reality–dealing with complexity Object-based image analysis (pp. 3-27): Springer. Li, M., Zang, S., Zhang, B., Li, S., & Wu, C. (2014). A review of remote sensing image classification techniques: The role of spatio-contextual information. European Journal of Remote Sensing, 47(1), pp. 389-411. Liddle, B. (2014). Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses. Population and Environment, 35(3), pp. 286-304. Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote sensing and image interpretation: John Wiley & Sons. Liu, Y., Wang, Y., Peng, J., Du, Y., Liu, X., Li, S., & Zhang, D. (2015). Correlations between urbanization and vegetation degradation across the world’s metropolises using DMSP/OLS nighttime light data. Remote Sensing, 7(2), pp. 2067-2088. López, E., Bocco, G., Mendoza, M., & Duhau, E. (2001). Predicting land-cover and land-use change in the urban fringe: a case in Morelia city, Mexico. Landscape and urban planning, 55(4), pp. 271-285. Luo, M., & Lau, N.-C. (2017). Heat waves in southern China: Synoptic behavior, long-term change, and urbanization effects. Journal of Climate, 30(2), pp. 703-720. Mahboob, M. A., Atif, I., & Iqbal, J. (2015). Remote sensing and GIS applications for assessment of urban sprawl in Karachi, Pakistan. Science, Technology and Development, 34(3), pp. 179-188. Mallinis, G., Koutsias, N., Tsakiri-Strati, M., & Karteris, M. (2008). Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS Journal of Photogrammetry and Remote Sensing, 63(2), pp. 237-250. Mas, J.-F., Velázquez, A., Díaz-Gallegos, J. R., Mayorga-Saucedo, R., Alcántara, C., Bocco, G., . . . Pérez-Vega, A. (2004). Assessing land use/cover changes: a nationwide multidate spatial database for Mexico. International Journal of Applied Earth Observation and Geoinformation, 5(4), pp. 249-261. Mathew, A., Chaudhary, R., Gupta, N., Khandelwal, S., & Kaul, N. (2015). Study of Urban Heat Island Effect on Ahmedabad City and Its Relationship with Urbanization and Vegetation Parameters. International Journal of Computer & Mathematical Science, 4, pp. 2347-2357. Megahed, Y., Cabral, P., Silva, J., & Caetano, M. (2015). Land cover mapping analysis and urban growth modelling using remote sensing techniques in greater Cairo region—Egypt. ISPRS International Journal of Geo-Information, 4(3), pp. 1750-1769. Metternicht, G. (2001). Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and GIS. Foundations of an expert system. Ecological modelling, 144(2-3), pp. 163-179. Miller, R. B., & Small, C. (2003). Cities from space: potential applications of remote sensing in urban environmental research and policy. Environmental Science & Policy, 6(2), pp. 129-137. Mirzaei, P. A. (2015). Recent challenges in modeling of urban heat island. Sustainable Cities and Society, 19, pp. 200-206. Mohammed, I., Aboh, H., & Emenike, E. (2007). A regional geoelectric investigation for groundwater exploration in Minna area, north west Nigeria. Science World Journal, 2(4) Morenikeji, G., Umaru, E., Liman, S., & Ajagbe, M. (2015). Application of Remote Sensing and Geographic Information System in Monitoring the Dynamics of Landuse in Minna, Nigeria. International Journal of Academic Research in Business and Social Sciences, 5(6), pp. 320-337. Mukherjee, A. B., Krishna, A. P., & Patel, N. (2018). Application of Remote Sensing Technology, GIS and AHP-TOPSIS Model to Quantify Urban Landscape Vulnerability to Land Use Transformation Information and Communication Technology for Sustainable Development (pp. 31-40): Springer. Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), pp. 1145-1161. Nemmour, H., & Chibani, Y. (2006). Multiple support vector machines for land cover change detection: An application for mapping urban extensions. ISPRS Journal of Photogrammetry and Remote Sensing, 61(2), pp. 125-133. Niu, X., & Ban, Y. (2013). Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach. International journal of remote sensing, 34(1), pp. 1-26. Nogueira, K., Penatti, O. A., & dos Santos, J. A. (2017). Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, pp. 539-556. Oguz, H., & Zengin, M. (2011). Analyzing land use/land cover change using remote sensing data and landscape structure metrics: a case study of Erzurum, Turkey. Fresenius Environmental Bulletin, 20(12), pp. 3258-3269. Pohl, C., & Van Genderen, J. L. (1998). Review article multisensor image fusion in remote sensing: concepts, methods and applications. International journal of remote sensing, 19(5), pp. 823-854. Price, O., & Bradstock, R. (2014). Countervailing effects of urbanization and vegetation extent on fire frequency on the Wildland Urban Interface: Disentangling fuel and ignition effects. Landscape and urban planning, 130, pp. 81-88. Prosdocimi, I., Kjeldsen, T., & Miller, J. (2015). Detection and attribution of urbanization effect on flood extremes using nonstationary flood‐frequency models. Water resources research, 51(6), pp. 4244-4262. Rawat, J., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), pp. 77-84. Rokni, K., Ahmad, A., Solaimani, K., & Hazini, S. (2015). A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques. International Journal of Applied Earth Observation and Geoinformation, 34, pp. 226-234. Sakieh, Y., Amiri, B. J., Danekar, A., Feghhi, J., & Dezhkam, S. (2015). Simulating urban expansion and scenario prediction using a cellular automata urban growth model, SLEUTH, through a case study of Karaj City, Iran. Journal of Housing and the Built Environment, 30(4), pp. 591-611. Santra, A. (2016). Land Surface Temperature Estimation and Urban Heat Island Detection: A Remote Sensing Perspective. Remote Sensing Techniques and GIS Applications in Earth and Environmental Studies, p 16. Shrivastava, L., & Nag, S. (2017). MONITORING OF LAND USE/LAND COVER CHANGE USING GIS AND REMOTE SENSING TECHNIQUES: A CASE STUDY OF SAGAR RIVER WATERSHED, TRIBUTARY OF WAINGANGA RIVER OF MADHYA PRADESH, INDIA. Shuaibu, M., & Sulaiman, I. (2012). Application of remote sensing and GIS in land cover change detection in Mubi, Adamawa State, Nigeria. J Technol Educ Res, 5, pp. 43-55. Song, B., Li, J., Dalla Mura, M., Li, P., Plaza, A., Bioucas-Dias, J. M., . . . Chanussot, J. (2014). Remotely sensed image classification using sparse representations of morphological attribute profiles. IEEE transactions on geoscience and remote sensing, 52(8), pp. 5122-5136. Song, X.-P., Sexton, J. O., Huang, C., Channan, S., & Townshend, J. R. (2016). Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover. Remote Sensing of Environment, 175, pp. 1-13. Tayyebi, A., Shafizadeh-Moghadam, H., & Tayyebi, A. H. (2018). Analyzing long-term spatio-temporal patterns of land surface temperature in response to rapid urbanization in the mega-city of Tehran. Land Use Policy, 71, pp. 459-469. Teodoro, A. C., Gutierres, F., Gomes, P., & Rocha, J. (2018). Remote Sensing Data and Image Classification Algorithms in the Identification of Beach Patterns Beach Management Tools-Concepts, Methodologies and Case Studies (pp. 579-587): Springer. Toth, C., & Jóźków, G. (2016). Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 115, pp. 22-36. Tuholske, C., Tane, Z., López-Carr, D., Roberts, D., & Cassels, S. (2017). Thirty years of land use/cover change in the Caribbean: Assessing the relationship between urbanization and mangrove loss in Roatán, Honduras. Applied Geography, 88, pp. 84-93. Tuia, D., Flamary, R., & Courty, N. (2015). Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions. ISPRS Journal of Photogrammetry and Remote Sensing, 105, pp. 272-285. Tzotsos, A., & Argialas, D. (2008). Support vector machine classification for object-based image analysis Object-Based Image Analysis (pp. 663-677): Springer. Wang, L., Sousa, W., & Gong, P. (2004). Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International journal of remote sensing, 25(24), pp. 5655-5668. Wang, Q., Zeng, Y.-e., & Wu, B.-w. (2016). Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China. Renewable and Sustainable Energy Reviews, 54, pp. 1563-1579. Wang, S., Ma, H., & Zhao, Y. (2014). Exploring the relationship between urbanization and the eco-environment—A case study of Beijing–Tianjin–Hebei region. Ecological Indicators, 45, pp. 171-183. Weitkamp, C. (2006). Lidar: range-resolved optical remote sensing of the atmosphere: Springer Science & Business. Wellmann, T., Haase, D., Knapp, S., Salbach, C., Selsam, P., & Lausch, A. (2018). Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing. Ecological Indicators, 85, pp. 190-203. Whiteside, T. G., Boggs, G. S., & Maier, S. W. (2011). Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation, 13(6), pp. 884-893. Willhauck, G., Schneider, T., De Kok, R., & Ammer, U. (2000). Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos. Proceedings of XIX ISPRS congress. Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., . . . Young, S. A. (2009). Overview of the CALIPSO mission and CALIOP data processing algorithms. Journal of Atmospheric and Oceanic Technology, 26(11), pp. 2310-2323. Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E., & Tucker III, C. J. (2015). Use of the Normalized Difference Vegetation Index (NDVI) to Assess Land Degradation at Multiple Scales: Current Status, Future Trends, and Practical Considerations: Springer. Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., & Schirokauer, D. (2006). Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing, 72(7), pp. 799-811. Zhou, D., Zhao, S., Zhang, L., & Liu, S. (2016). Remotely sensed assessment of urbanization effects on vegetation phenology in China's 32 major cities. Remote Sensing of Environment, 176, pp. 272-281. Zhu, Z., Fu, Y., Woodcock, C. E., Olofsson, P., Vogelmann, J. E., Holden, C., . . . Yu, Y. (2016). Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014). Remote Sensing of Environment, 185, pp. 243-257.

Abstract Horizontal wells provide a highly efficient way to maximize contact with the reservoir target and to increase overall recovery by allowing a larger drainage pattern. Traditionally, the identification of optimal horizontal well locations involves domain expertise across multiple disciplines and takes a long time to complete. In this work, a fully streamlined artificial intelligence (AI)-based workflow is introduced to facilitate horizontal opportunity identification by combining geological and engineering attributes in all types of reservoirs. This workflow relies on automated geologic and engineering workflows to map the remaining oil in place and identify areas with high probability of success (POS) and high productivity potential. Advanced computational algorithms are implemented under a variety of physical constraints to identify best segments for placing the wellbores. Statistical and machine learning techniques are combined to assess neighborhood performance and geologic risks, along with forecasting the future production performance of the proposed targets. Finally, a comprehensive vetting and sorting framework is presented to ensure the final set of identified opportunities are feasible for the field development plan. The workflow incorporates multiple configuration and trajectory constraints for the horizontal wells’ placement, such as length/azimuth/inclination range, zone-crossing, fault-avoidance, etc. The optimization engine is initialized with an ensemble of initial guesses generated with Latin-Hypercube Sampling (LHS) to ensure all regions of good POS distribution in the model are evenly considered. The intelligent mapping between discrete grid indexing and continuous spatial coordinates greatly reduced the timing and computational resources required for the optimization, thus enabling a fast determination of target segments for multi- million-cell models. The optimization algorithm identifies potential target locations with 3D pay tracking globally, and the segments are further optimized using an interference analysis that selects the best set of non-interfering targets to maximize production. This framework has been successfully applied to multiple giant mature assets in the Middle East, North and South America, with massive dataset and complexity, and in situations where static and dynamic reservoir models are unavailable, partially available, or are out of date. In the specific case study presented here, the workflow is applied to a giant field in the Middle East where tens of deviated or horizontal opportunities are initially identified and vetted. The methodology presented turns the traditional labor-intensive task of horizontal target identification into an intelligently automated workflow with high accuracy. The implemented optimization engine, along with other features highlighted within, has enabled a lightning-fast, highly customizable workflow to identify initial opportunity inventory under high geological complexity and massive dataset across different disciplines. Furthermore, the data-driven core algorithm minimizes human biases and subjectivity and allows for repeatable analysis.

Abstract Appraisal drilling of a recently discovered Cambrian-aged gas field in Oman is indicating that the field may have significant amounts of gas locked in a challenging deep, hot, and highly pressured reservoir environment. The low porosity and permeability values of the Amin reservoir allow the classification of the reservoir as a tight gas sand. The variability of reservoir properties, both spatially and vertically, makes it difficult to standardize perforation and fracture stimulation design which, in turn, complicates delineation of a development plan for the project. One of the difficulties relates to uncertainty in vertical propagation of hydraulic fractures. Fracture height based on evaluation of radioactive tracer logs indicates that vertical barriers to fracture propagation may relate to specific geologic zones in the reservoir. The mapping of the reservoir zones into undeveloped areas of the field would allow selection of primary and secondary production targets based on the specific physical properties of the individual zones. To assume that no barrier to fracture propagation exists between separate production units may lead to attempts to stimulate them synchronously, which would be disadvantageous for several reasons, such as premature screenouts and incomplete coverage of gas-bearing layers. Reserves booking and allocation can also be jeopardized should the fractures propagate into undesired zones.

Abstract This study summarizes the efforts taken to provide reliable reservoir characterizations products to mitigate seismic interpretation challenges and delineation of the reservoirs. ADNOC has conducted seismic exploration activities to assess Miocene to Upper Cretaceous aged reservoirs in East Onshore Abu Dhabi. The Oligo-Miocene section comprises of interbedded salt (mainly halite), anhydrite, limestones and marls. Deposited in the foreland basin related to the Oman thrust-belt. Ranging in thickness from nearly 1.5 km in the depocenter to almost nil on the forebulge located to the west of the studied area. The well data based geological model suggests that initially porous rocks (presumably grain-supported carbonates) encompassed polyphase sulfate cementation during recurrent subaerial exposure in which pores and grains were recrystallized sometimes completely too massive, tight anhydrite beds. This heterogeneity of the complex shallow section showing high variation of velocity impact seismic imaging, and interpretation to model the stratigraphic/structural framework and link it with reservoir characterization. Hence, ADNOC decided to conduct a trial on state-of-art technique Litho-Petro-Elastic (LPE) AVA Inversion to mitigate the seismic interpretation challenges and delineate the reservoirs. The LPE AVA inversion provides a single-loop approach to reservoir characterization based on rock physics models and compaction trends, reducing the dependency on a detailed prior the low frequency model, Where the rock modelling and lithology classification are not separate steps but interact directly with the seismic AVO inversion for optimal estimates of lithologies and elastic properties. The LPE inversion scope requires seismic data conditioning such as CMP gathers de-noising, de-multiple, flattening and amplitude preservation, in addition to detailed log conditioning, petro-elastic and rock physics analysis to maximize the quality and value of the results. The study proved that the LPE AVA Inversion can be used to guide seismic interpreters in mapping the structural framework in challenging seismic data, as it managed to improve the prospect evaluation.