dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012; Fei et al. , 2016; Gomes et al. , 2018; Usman et al. , 2021). More recently, the geocellular modeling of different lithofacies from outcrop analogues has been widely applied to the fluvial braided systems based on subsurface data  (Pringle et al. , 2006; Howell et al. , 2014) , but petrophysical heterogeneity in the thin beds remains unsolved through surface data. Conventionally, seismic, well logs, and petrophysical datasets are useful for reservoir and petrophysical modeling  (Henriquez and Jourdan, 1995; Charles, 1999; Brandsӕ; ter et al. , 2005)  but limited to spatial variations and thin beds resolution in the reservoir media  (Rarity et al. , 2014). Regarding this, the digital scanning of outcrop analogues plays a dynamic protagonist in the examination of reservoir heterogeneities with petrophysical modeling in thin bedded media  (Fabuel-Perez et al.
, 2010; Pavlis and Bruhn, 2011; Pavlis and Mason, 2017). The digital scanning of outcrops through LiDAR provides quantitative data (e. g. , point clouds, photorealistic 3D models, and measurements with orientations, polylines) for the lithofacies (geobodies) modeling  (Notebaert et al. , 2009; Fabuel-Perez et al. , 2009b; 2010)  to demonstrate the heterogeneity with spatial and temporal variations of petro-physical data  (Immenhauser et al. , 2004; Mikes and Geel, 2006; Jones et al. , 2008a; Amour et al. , 2012). Acquisition of the outcrop analogues is usually performed through scanning techniques, such as the Light Detection and Ranging (LiDAR)  (Xu et al. , 2000; Slob and Hack, 2004; Bellian et al. , 2005; Bellian et al. , 2007; Siddiqui et al. , 2019) , which acquires outcrop dataset in the form of point clouds and photorealistic 3D models for virtual outcrop modeling (VOM)  (Enge et al.
, 2009b; 2010; Siddiqui et al. , 2019). This shows that the flow simulation in reservoir modeling depends on the qualitative and quantitative datasets in the VOM to elucidate the petrophysical heterogeneities of lithofacies  (Kjønsvik et al. , 1994; Løseth et al. , 2004; Adams et al. , 2005; Falivene et al. , 2006b; Eaton, 2006; Aigner et al. , 2007; Qi et al. , 2007; Phelps et al. , 2008; Verwer et al. , 2009; Palermo et al. , 2010; Tom as et al. , 2010; Amour et al. , 2012; Wilkinson et al. , 2016). Whereas, for dynamic flow simulation in reservoir modeling, algorithms that are supportive in stochastic lithofacies are applied to different reservoir settings e. g. , in channel-fill turbidites  (Falivene et al. , 2006b). These algorithms including Truncated Gaussian Simulation (TGSim), Sequential Indicator Simulation (SISim) and Indicator Kriging (IK)  (White et al.
, 2012 , Menier et al. , 2017; Siddiqui et al. , 2019 Siddiqui et al. , , 2020;; Usman et al. , 2020a, b, c) but  have not yet any avenues for 3D geomodeling to demonstrate the spatial and temporal heterogeneity in the siliciclastic rock types for reservoir study. The main purpose of this study is to elaborate the integrated studies of reservoir in the 3D geo-cellular modeling based on outcrop analogues through LiDAR for VOM to demonstrate the petrophysical and heterogenetic characteristics of well exposed reservoir rocks of Sandakan Formation, NW, Borneo (Fig. 1 ). All input datasets for the interpretation of lithofacies, their horizons, pseudo wells in the Virtual Reality Geological Studio (VRGS) and an industrial reservoir modeling software (Petrel™ Suite) were collected through conventional fieldwork and LiDAR scanning and data processing of different sections in northwest Borneo formation (Fig.
Methodology and workflow 3. 1. Fieldwork for sedimentological architecture and core plugs Four (4) well-exposed outcrop sections to the northwest part of Borneo (Fig. 1a ) were chosen for field based sedimentological architecture study, and thirty (30) core plugs were plugged out from these sections with handheld coring machine. For sedimentological architecture, we measured the thickness of the sections with grain size variations and recorded primary sedimentary structures and borrowing index. Following facies scheme of Siddique et al. 2019, we marked six (6) outcrop lithofacies of Sandakan Formation for further 3D geo-cellular and geo-modeling studies. Porosity and permeability of all recovered core plugs from these lithofacies were analyzed using Helium Porosimeter and Helium Poroperm to document the petro-physical heterogeneity in the geo-cellular model with pseudo wells using reservoir modeling software Pe-trel™ Suite (Fig.
Methodology: Creation of Virtual Outcrop Modeling (VOM) Well-exposed outcrops with lateral and vertical extensions are good representatives for reservoir modeling  (Arbu es et al. , 2007; Fabuel-Perez et al. , 2009b; 2010). Digital scanning techniques have been developed to construct high-resolution outcrop analogues for better understanding of reservoirs  (Pringle et al. , 2006; Enge et al. , 2007; Gold et al. , 2012). We used RIEGL VZ-2000i long range 3D Light detection and ranging (LiDAR) laser scanner of LMS-Z series to acquire the point cloud dataset with 3D photorealists images from the four outcrops of the Sandakan Formation, northwest Borneo for virtual reality modeling (VRM). LiDAR was chosen for its effectiveness in mapping and digitization  (Gold et al. , 2012; Buckley et al. , 2016; De Paor, 2016)  in the regions with very thick vegetation and/or difficult to access for the three-dimensional (3D) outcrops modeling.
For the virtual outcrop modeling (VOM), the LiDAR dataset consists of field data and digital scanning data of the measured sections with dGPS points and scanned photographs with maximum ranging of the features. The sedimentological features including sedimentary structures with paleocurrent measurements and lithofacies with the variations of grain size were recorded in the form of logs. Both sedimentological and LiDAR data further were used in Virtual Reality Geological Studio (VRGS) to interpret and digitize the identified lithofacies within stratigraphic horizon framework, and then were processed to make the VOMs for further interpretation and modeling.
Data acquisition and processing Details of collecting and processing of point cloud dataset of the LiDAR have been described by many researchers (i. e. ,  Pringle et al. , 2006; Bellian et al. , 2005; Enge et al. , 2007; Buckley et al. , 2008; Kurz et al. , 2011; Gold et al. , 2012; Sima, 2013 , Howell et al. , 2014; Siddiqui et al. , 2019)  for reservoir modeling. The acquired data from LiDAR  (Siddiqui et al. , 2019)  was then re-processed by using RiScan Pro v2. 1. 1 software according to the Fabuel-Perez et al. (  2010 ) for the 10,000 points/second that were interdepended on the rock reflectivity and weathering with ±0. 01 mm accuracy. 3. 2. 1. 1. Pre-processing of dataset. The LiDAR Point-cloud data were acquired with 8-scan positioning on the 250 m 2 thick succession of the Sandakan Formation, northwest Borneo.
The data contain 210 million points in x, y, z format that based on outcrop 3. 2. 1. 2. Post-processing. DGPS information was used to provide the globally correct scanning positions according to UTM (Universal Transverse Mercator) coordinate system in the pre-processed dataset. All vegetations and noises were removed by using the Object Manager and Echo options for further processing. The cleaned scans were triangulated to get the mesh file on which 3D photorealistic images of panoramic views were dressed up. To reduce the numbers of triangles in the mesh file, the mesh decimation process was subjected without changing of geometry and shape of the final model (Fig. 2 ). In the end, to get the original color of scans each vertex was mapped in the mesh back that was textured with digital images.
Generation of geo-cellular and petrophysical modeling (geomodeling) VOM analogues were used as input dataset for the geo-cellular modeling that is a 3D gridding volume of the reservoir media with the complete illustration of stratigraphic strata interval, reservoir sublayers/horizons and faults in the form of 3D cells meshes  (Shepherd, 2009; Yarus et al. , 2012; Fei et al. , 2016; Gomes et al. , 2018). The analogue based geo-modeling can be improved by increasing the resolution and removing of the uncertainties  (Jackson et al. , 2005; Coburn et al. , 2006; Fabuel-Perez et al. , 2009b; 2010; Siddiqui et al. , 2019). The required dataset for high resolution reservoir with limited uncertainties for 3D geo-cellular gridding and geo-modeling can be seen in detail from  Fabuel-Perez et al. (2009b , 2010).
The recommended dataset is used for 3D geomodeling of lithofacies with the help of marked horizons that extracted from the processed VRGS and interpreted on VOMs (Figs. 2  and 3 ). The extracted VOMs data (horizons and dGPS analogue data) from the marked VRGS lithofacies were imported into the Petrel™ Suite for the further interpretations, 3-D modeling, and flow stochastic simulation (Fig. 3 ). The geo-modeling of the reservoir is very helpful to understand and visualize the subsurface events, reservoir property and its petrophysical heterogeneity  (Bryant et al. , 2000; Fabuel-Perez et al. , 2009b , 2010; Rarity et al. , 2014).
Pseudo wells Field based sedimentary logs were interpreted in VRGS software to make the pseudo wells for digital lithology, grain size, thickness, sedimentary structures and paleocurrent directions with the accurate coordinates. This georeferenced sedimentary logs data was imported into the Petrel™  (Fabuel-Perez et al. , 2010; Rarity et al. , 2014)  as pseudo wells in x, y, z format to generate the horizons and sub-horizons. The pseudo wells data is used to improve the sedimentological details in lithofacies modeling, to confirm the geo-cellular modeling with respect to horizons and sub-horizons and also to control the qualitative measurements of the geocellular model. Four pseudo wells with all interpreted properties of measured sections with vertically thickness were imported into the Petrel™ Suite.
3-D geo-cellular modeling and lithofacies gridding Gridding is the first step to generate the 3D geo-cellular model of lithofacies. We used stratigraphic surfaces and sub-horizons (layering) in the geo-cellular model to build 3-D lithofacies gridding in the Petrel™ Suite. To make it more realistic, we used corner coordinates of the VOMs outcrop model and porosity and permeability for the petrophysical model. The geo-cellular model covers the three dimensional (3D) large area in X, Y and Z directions  (Fabuel-Perez et al. , 2010; Howell et al. , 2014). X and Y directions show the heterogeneities in the reservoir media and Z direction shows the vertical resolution that was extrapolated in the modeling software for higher resolution and better studies to know the temporal and spatial variations in lithofacies model.
Stochastic modeling: petrophysical modeling Stochastic modeling is based on three types of simulation algorithms: Truncated Gaussian Simulation (TGSim), Sequential Indicator Simulation (SISim), and Indicator Kriging (IK) in the reservoir modeling Petrel™ software for different models with same datasets  (Gotway and Rutherford, 1994; Bastante et al. , 2008). In this study, SISim has been used for 3D lithofacies, and highresolution subsurface thinly bedded reservoir in terms of petrophysical heterogeneities in the reservoir model. Upscaling of the pseudo wells with the SISim was done according to the lithofacies thickness to mark sub-horizons in the key horizon of same facies for high resolution of heterogeneity in geo-cellular model.
LiDAR and VRGS based virtual outcrop modeling (VOM) Three (3) out of four (4) well-exposed outcrops sections were selected for the LiDAR scanning. The acquired data in the form of thousands of point clouds were processed in the RiScan Pro software to get the textural digital model. Digital triangulated meshed model of outcrop in the form of virtual outcrop modeling (VOM) (Fig. 5 ) was built from RiScan Pro software after all processing steps (see details in section 3. 2). To interpret the sedimentological parameterization of the outcrops, the high-resolution digital model from RiScan Pro was imported into the Virtual Reality Geological Studio (VRGS) software in the form of LAS file. Analogues of sedimentological parameterization of architectural studies are (Fig. 5):  (1) sedimentological logs, including grain size, lithology, bed thickness, facies association and sedimentary structures; and (2) building horizons and digitizing according to the marked lithofacies of the Sandakan Formation.
Input data integration as pseudo wells for 3D geo-modeling and quantitative analysis Integration of scanned and digital dataset is very important for the 3D geo-modeling analysis through geo-cellular and petrophysical models  (Pringle et al. , 2006). The input dataset includes a suite of 1D georeferenced outcrop sections and well logs, photorealists images, horizons interpretations, dGPS dataset, point cloud data with textured surfaces through LiDAR and RiScan Pro as VOMs and geophysical dataset. The obtained virtual models were then manually interpreted to show the actual and real field-based facies variations (Fig. 5 ), association and their thickness from the lateral connectivity of sedimentary logs (Fig. 6 ) as an input dataset for modeling. In addition, sedimentological parameterization including facies distribution (Fig.
6 ), bedding features (dip, strike, paleocurrent flow direction) were extracted from pseudo wells and field photographs of  Siddiqui et al. (2019). The input dataset as; georeferenced sedimentary logs (pseudo wells), point sets (X, Y, Z format) of interpreted horizons imported into the Petrel™ software to obtain spatial models. Reservoir 2D connectivity in the lithofacies and uncertainties in 2D flow simulation model were estimated from  Siddiqui et al. (2019). All obtained datasets from VOMs and photorealistic images with sedimentological studies, horizons and pseudo wells were populated in the Petrel™ according to   Pringle et al. (2006)  to gain facies-based 3D geo-cellular model (Fig. 7 ) for the generation of petrophysical model to improve the understanding of lithofacies with reservoir flow simulation.
Once all the input dataset has populated, we need the porosity and permeability ranges for all lithofacies in 3D geo-cellular model that has been discussed in detail as Section 3. 4. According to the show the sedimentary structures (PLS, CBS, MS, BS, TCBS) that formed in the interpreted lithofacies (aee). The digitized lithofacies in VRGS with the sedimentary logs and virtual scales were used to measure the thickness and lateral heterogeneity in the measured section. On the left side, the real automatically generated georeferenced sedimentary log with actual grain size, lithology, bed thickness, facies association, and sedimentary structures with different colors. quantitative analysis of porosity and permeability in the geomodeling, the good reservoir lithofacies are CBS because these lithofacies have higher porosity and permeability ranging from 25.
3D geo-cellular model for spatial distribution of lithofacies 3D geo-cellular model was obtained from all the input dataset (pseudo wells, point sets (X, Y, Z format) of interpreted horizons) in the Petrel™. For the zonation of the geo-cellular model, lithofacies information in the pseudo logs was upscaled according to the thickness to show the thin beds within the same lithofacies model (Fig. 7b  and c ). The object-based technique in the modelling software was used to illustrate the shape and dimensions of input parameters (Table  1 ) for 3D geo-cellular facies model  (Deutsch and Wang, 1996; Deutsch and Tran, 2002; Falivene et al. , 2007; Fabuel-Perez et al. , 2010). 3D geo-cellular lithofacies model was interpolated and extrapolated in Petrel™, according to coordinates of the study area and interpreted key surfaces respectively (Fig.
VOMs and pseudo wells dataset in geo-modeling Field based detailed studies of outcrop sections for lithofacies variations with the enhancement of geostatistical and analogue dataset reduce the uncertainties in the modeling  (Narayanan et al. , 1999; Pringle et al. , 2006; Enge et al. , 2007; Siddiqui et al. , 2019). LiDAR based digital and virtual modeling of outcrops (VOMs) provide highly geospatial input dataset that reduces the uncertainties from 10's of km to cm  (Enge et al. , 2007; Rarity et al. , 2014). That can be used for the industry with the integration of seismic and pseudo well logs in the modeling software by filling up the resolution scale between VOMs and conventional geophysical data  (Brandsaeter et al. , 2005; Jackson et al. , 2005; Coburn et al. , 2006; Fabuel-Perez et al.
, 2009b; 2010; Rarity et al. , 2014; Siddiqui et al. , 2019). In this study, the digitized outcrops and pseudo well logs (Figs. 5  and 6 ) were used to integrate the petrophysical lithofacies model in the Petrel™ to demonstrate the reservoir heterogeneity with respect to porosity and permeability in thin beds (Fig. 7 ). The input dataset for this modeling covers all geological characteristics and also maintained for long outcrops as  Pringle et al. (2006); Fabuel-Perez et al. (2009b , 2010) ;  Rarity et al. (2014) ;  Siddiqui et al. (2019). We have used VRGS software for 3D interpretation of the geostatistical dataset including key horizons and pseudo wells    2014 ), to generate three-dimensional models. In the 3D geo-cellular and petrophysical well logs model  (Figs. 7c, 8, 9) , the association and distributions of the lithofacies correspond to the VOM of the study that shows high precision of the dataset and it has been discussed by many researchers in their studies of the VOMs of sandstone facies architecture  (Fabuel-Perez et al.
, 2009b , 2010; Rarity et al. , 2014; Howell et al. , 2014; Newell and Shariatipour, 2016; Seers, 2017; Siddiqui et al. , 2019). The most important contribution in the study is the spatial elucidation of thin beds in the 3D geo-model for the petrophysical characteristics and heterogeneities. Previous studies were only done just for the 2D connectivity of thin beds with the static model  (Siddiqui et al. , 2019). For better understanding and elucidation, object-based technique was used for each lithofacies to improve the shape and dimensions of LiDAR based input dataset for all lithofacies  (Deutsch and Wang, 1996; Deutsch and Tran, 2002; Falivene et al. , 2007; Fabuel-Perez et al. , 2010; Siddiqui et al. , 2019). According to the 3D geo-model, sandstone rich facies (CBS, PLS, MWLS) are overall good reservoir due to high porosity and permeability from 27.
Uncertainties and limitations of input analogue dataset This work was done to improve the applications of the input analogue dataset of outcrops with the help of virtual outcrop modeling (VOM) based on VRGS software. We followed the workflow of previous researchers  (Fabuel-Perez et al. , 2009b; 2010; Rarity et al. , 2014; Howell et al. , 2014)  in addition to extemporizing the experimental petrophysical (porosity and permeability) values in the industry-based reservoir modeling software Petrel™ with geostatistical dataset. The VOMs dataset help to reduce the uncertainties in the surface-based 3D geo-cellular and petrophysical modeling of the reservoir facies  (Fabuel-Perez et al. , 2009b; 2010; Rarity et al. , 2014). But still, there are main uncertainties in the extrapolation of input dataset of 3D geo-modeling as 3D gridding cells with petrophysical properties that have been described.
Uncertainties related to input dataset and limitations: interpretation of the VOMs analogues is tenuous work  (Fabuel-Perez et al. , 2009b , 2010; Howell et al. , 2014; Rarity et al. , 2014; Siddiqui et al. , 2019)  to deal with, we scanned 3 outcrop sections to get enough data and do the interpretation for lateral and vertical extension of the lithofacies VRGS software used according to  Fabuel-Perez et al. (2010). Still, there is a limitation of the scanned dataset for the 3D geo-modeling to describe the subsurface reservoir heterogeneities. Uncertainties of 3D geo-modeling for the laterally and vertical spatial distribution of the reservoir facies, Petrel™ software provides enough illustration but still some ambiguities and complexities remain in the final model as gridding cells  (Fabuel-Perez et al.
Conclusions The integrated studies of the surface-based geostatistical analogues and field sedimentary data give a procedure of the interpretation for the reservoir properties and heterogeneities in petrophysical modeling on Sandakan Formation, NW, Borneo. The geostatistical data of the VOMs shows the propagation of the key horizons (stratigraphic surfaces) and their sub horizons (zonation) for 3D geo-cellular modeling of lithofacies in the Petrel™ suite. This integration of VOMs dataset for the 3D geo-cellular modeling with the reservoir properties allows to elucidate the subsurface modeling based on the high-resolution sedimentological dataset. The reservoir properties of the 3D modeled lithofacies in the Pe-trel™ software show that CBS, PLS and MWLS have good to fair reservoir quality with heterogenous porosity and permeability from 27.
0% to 20. 2% and 74. 03 mD to 9. 08 mD, respectively. In the 3D geo-cellular and petrophysical model, other lithofacies have poor reservoir quality due to the high baffling of surrounding sediments of mudstone and sand particles, which was studied in the high-resolution dataset. This modeling study shows the 3D dynamic spatial connectivity of all lithofacies that were 2D in the outcrop sections. Based on reservoir properties in the 3D petrophysical model, there are strong heterogeneities of porosity and permeability in the Sandakan Formation that would influence the reservoir quality within the Sandakan sub-basin reservoirs. There are some uncertainties in the surface-based 3D reservoir modeling dataset that shows errors in the resultant 3D geo-cellular and petrophysical model that can be decreased by increasing the sample size of scanning positions and with the integration of subsurface dataset.
ACKNOWLEDGMENTS We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modeling, which is in charge of the fifth Coupled Model Intercomparison Project, and we thank the climate modeling groups for producing and making available their model output. We acknowledge the  Integrated Climate Data Center (ICDC, icdc. cen. uni-hamburg. de)University of Hamburg, Hamburg, Germany , for distributing the regional sea level data from IPCC AR5 as well as Kopp et al. (2014), Nauels et al. (2017), andBamber et al. (2019) for making their data available.
FUNDING This study benefited from the  IPSL Prodiguer-Ciclad  facility is supported by  CNRS ,  UPMC ,  Labex L-IPSL  (Grant # ANR-10-LABX-0018 ), and the  European FP7   IS-ENES2  project (Grant # 312979 ). This study was supported by a grant from the  French Ministry for an Ecological  and  Solidary Transition as part of the Convention on financial support for climate services  and the  ANR   Storisk  project ( ANR-15-CE03-0003 ).
Numerical Model Description The Mike 21/3 model proposed by the Danish Hydraulic Institute (DHI) was applied to gain insight into the hydrodynamics at river mouths over a full range of conditions with different idealized river mouth bathymetries. The Mike 21/3 package used in this study comprises the following modules: Mike 3 FM (Flow module), Mike 21 SW (Spectral waves), with the capability of running in coupled mode. Mike 3 FM is based on the three-dimensional incompressible Reynolds-averaged Navier-Stokes equations with the assumptions of Boussinesq and hydrostatic pressure. It consists of continuity, momentum, temperature, salinity, and density equations with multiple models for turbulence  (DHI, 2017a). Mike 21 SW is capable of simulating wave growth due to wind action, propagation in deep and nearshore waters, breaking, dissipation, refraction, diffraction and shoaling, non-linear wavewave interactions, wave-current interactions (refraction and whitecapping), dissipation due to whitecapping, bottom friction, and depth-induced wave breaking  (DHI, 2017b).