scholarly journals Quantitative methods to direct exploration based on hydrogeologic information

2006 ◽  
Vol 8 (2) ◽  
pp. 77-90 ◽  
Author(s):  
Andrew J. Graettinger ◽  
Jejung Lee ◽  
Howard W. Reeves ◽  
Deepu Dethan

Quantitatively Directed Exploration (QDE) approaches based on information such as model sensitivity, input data covariance and model output covariance are presented. Seven approaches for directing exploration are developed, applied, and evaluated on a synthetic hydrogeologic site. The QDE approaches evaluate input information uncertainty, subsurface model sensitivity and, most importantly, output covariance to identify the next location to sample. Spatial input parameter values and covariances are calculated with the multivariate conditional probability calculation from a limited number of samples. A variogram structure is used during data extrapolation to describe the spatial continuity, or correlation, of subsurface information. Model sensitivity can be determined by perturbing input data and evaluating output response or, as in this work, sensitivities can be programmed directly into an analysis model. Output covariance is calculated by the First-Order Second Moment (FOSM) method, which combines the covariance of input information with model sensitivity. A groundwater flow example, modeled in MODFLOW-2000, is chosen to demonstrate the seven QDE approaches. MODFLOW-2000 is used to obtain the piezometric head and the model sensitivity simultaneously. The seven QDE approaches are evaluated based on the accuracy of the modeled piezometric head after information from a QDE sample is added. For the synthetic site used in this study, the QDE approach that identifies the location of hydraulic conductivity that contributes the most to the overall piezometric head variance proved to be the best method to quantitatively direct exploration.

2018 ◽  
Vol 19 (2) ◽  
pp. 61
Author(s):  
Rusmawan Suwarman ◽  
Dinda Mahardita ◽  
I Dewa Gede A. Junnaedhi

Estimasi evaporasi di daerah waduk menggunakan metode empiris dengan input data satelit dilakukan untuk mengatasi masalah ketersediaan data meteorologi dari observasi permukaan. Data satelit berupa Land Surface Temperature dari satelit Himawari dan profil atmosfer dari satelit MODIS digunakan untuk memperoleh informasi parameter temperatur, kelembapan relatif dan radiasi matahari untuk mengestimasi besaran evaporasi di daerah waduk. Metode empiris yang digunakan antara lain adalah Blaney-Criddle, Kharuffa, Hargreaves, Schendel dan Schendel yang dimodifikasi (Modified Schendel). Hasil estimasi evaporasi dibandingkan terhadap evaporasi acuan yang dihitung menggunakan metode kombinasi (Penman) dengan input parameter meteorologi hasil observasi. Observasi dilakukan menggunakan Automatic Weather Station di dua titik pengamatan di Waduk Saguling. Hasil penelitian menunjukkan estimasi evaporasi waduk dengan input data satelit dapat dilakukan dengan metode yang ada namun diperlukan modifikasi. Metode estimasi evaporasi waduk yang terbaik adalah Modified Schendel, namun belum bisa menunjukkan variasi spasial yang sesuai observasi. Penggunaan regresi Linier Berganda dan menambahkan parameter radiasi matahari pada Modified Schendel, didapatkan suatu persamaan yang baik secara statistik dan dapat menunjukkan variasi spasial evaporasi di Waduk Saguling yang sesuai observasi.


2020 ◽  
Author(s):  
Eric Samakinwa ◽  
Christian Stepanek ◽  
Gerrit Lohmann

Abstract. In this study, we compare results obtained from modelling the mid-Pliocene warm period using the Community Earth System Models (COSMOS, version: COSMOS-landveg r2413, 2009) with the two different modelling methodologies and sets of boundary conditions prescribed for the two phases of the Pliocene Model Intercomparison Project (PlioMIP), tagged PlioMIP1 and PlioMIP2. Boundary conditions, model forcing, and modelling methodology for the two phases of PlioMIP differ considerably in palaeogeography, in particular with regards to the state of ocean gateways, ice-masks, treatment of vegetation and topography. Further differences between model setups as suggested for PlioMIP1 and PlioMIP2 consider updates to the concentration of trace gases: atmospheric carbon dioxide (CO2), is specified as 405 and 400 parts per million by volume (ppmv) for PlioMIP1 and PlioMIP2, respectively. There are also minor differences in the concentrations of methane (CH4) and nitrous oxide (N2O) due to changes in the protocol of the Paleoclimate Model Intercomparison Project (PMIP) from phase 3 to phase 4. Employing a single model across two phases of PlioMIP enables a better understanding of the impact that the various differences in modelling methodology between PlioMIP1 and PlioMIP2 have on model output. Yet, a dedicated comparison of COSMOS model output of PlioMIP1 and PlioMIP2 is not in the curriculum of model analyses proposed in PlioMIP2. Here, we bridge the gap between our contributions to PlioMIP1 (Stepanek and Lohmann, 2012) and PlioMIP2 (Stepanek et al., 2020). We highlight some of the effects that differences in the chosen mid-Pliocene model setup (PlioMIP2 vs. PlioMIP1) have on the climate state as derived with the COSMOS, as this information will be valuable in the framework of the model-model and model-data-comparison within PlioMIP2. We evaluate the model sensitivity to improved mid-Pliocene boundary conditions using PlioMIP's core mid-Pliocene experiments for PlioMIP1 and PlioMIP2, and present further simulations where we test model sensitivity to variations in palaeogeography, orbit and concentration of CO2. Firstly, we highlight major changes in boundary conditions from PlioMIP1 to PlioMIP2 and also the challenges recorded from the initial effort. The results derived from our simulations show that COSMOS simulates a mid-Pliocene climate state that is 0.29 K colder in PlioMIP2, if compared to PlioMIP1 (17.82 °C in PlioMIP1, 17.53 °C in PlioMIP2, values based on simulated surface skin temperature). On one hand, high-latitude warming, which is supported by proxy evidence of the mid-Pliocene, is underestimated in simulations of both PlioMIP1 and PlioMIP2. On the other hand, spatial variations in surface air temperature (SAT), sea surface temperature (SST) as well as the distribution of sea ice suggest improvement of simulated SAT and SST in PlioMIP2 if employing the updated palaeogeography. Our PlioMIP2 mid-Pliocene simulation produces warmer SSTs in the Arctic and North Atlantic Ocean than derived from the respective PlioMIP1 climate state. The difference in prescribed CO2 accounts for 1.1 K of warming in the Arctic, leading to an ice-free summer in the PlioMIP1 simulation, and a quasi ice-free summer in PlioMIP2. Beyond the official set of PlioMIP2 simulations, we present further simulations and analyses that sample the phase space of potential alternative orbital forcings that have acted during the Pliocene and may have impacted on geological records. Employing orbital forcing, which differ from that proposed for PlioMIP2 (i.e. corresponding to Pre-Industrial conditions) but falls into the Mid-Pliocene time period targeted in the PlioMIP, leads to pronounced annual and seasonal temperature variations, which are not directly retrievable from the marine and terrestrial reconstruction of the time-slice.


2021 ◽  
Author(s):  
I. Sumantri

BH field is one of the Globigerina limestone gas reservoir that exhibits strong seismic direct hydrocarbon indicator (DHI). This field is a 4-way dip faulted closure with Globigerina limestone as the main reservoir objective. The field was discovered back in 2011 by BH-1 exploration well and successfully penetrated about 350ft gross gas pay. BH-1 well was plugged and abandoned as Pliocene Globigerina limestone Mundu-Selorejo sequence gas discoveries. The laboratory analysis of sampled gas consists of 97.8% of CH4 and indicating a biogenic type of gas. This is the only exploration well drilled in this field and located on the crest of the structure. Seismic analysis both qualitative and quantitative, are common tools in delineating and characterizing reservoir. These methods usually make use of seismic data and well log collaboratively in the quest to reveal reservoir features internally. The lack of appraisal well in the area of study made the reservoir characterization process must be carried out thoroughly, incorporating several seismic datasets, both PSTM and PSDM, seismic gathers and stacks. Bounded by appraisal well limitation, this research looks into Gassmann's fluid substitution modeling, seismic forward modeling to confirm the DHI flat spot presence in the seismic, as well as seismic AVO analysis. Meanwhile, for quantitative analysis, model-based seismic post-stack inversion and simultaneous seismic pre-stack inversion were conducted in order to delineate the distribution of Globigerina limestone gas reservoir in BH Field. Through comprehensive analyses of qualitative and quantitative methods, this research may answer the challenge on how to intensively utilize seismic data to compensate the lack of appraisal well data in order to keep delivering a proper subsurface reservoir delineation.


2005 ◽  
Vol 110 (D14) ◽  
pp. n/a-n/a ◽  
Author(s):  
Joyce M. Harris ◽  
Roland R. Draxler ◽  
Samuel J. Oltmans

2019 ◽  
Vol 27 (2) ◽  
pp. 122-138 ◽  
Author(s):  
Vít Pászto ◽  
Karel Macků ◽  
Jaroslav Burian ◽  
Jiří Pánek ◽  
Pavel Tuček

Abstract The differences in welfare amongst European countries are especially evident in border regions, and this affects cross-border cooperation and relationships. Due to the historical development of Central and Eastern European countries over the last century, the affected countries are unique “laboratories” for geographical research. This study assesses disparities in socio-economic indicators representing socio-economic phenomena in the Czech-Polish border region, through the analysis of cross-border (spatial) continuity, using quantitative methods (multivariate statistics and socio-economic profiling), GIS analysis and cartographic visualisation. It is demonstrated how such a combination of methods is useful for the comparison and evaluation of the complex socio-economic situations in neighbouring countries. This research project identifies the most suitable common indicators for a proper evaluation of cross-border (spatial) continuity, and it reveals the spatial patterns as reflected by a cluster analysis. The greatest cross-border (spatial) continuity is apparent in the easternmost part of the borderlands, while significant differences on both sides of the border are evident in the very central part of the areas under study. The paper also describes methodological aspects of the research in order to provide a quantitative approach to borderland studies.


2013 ◽  
Vol 14 (2) ◽  
pp. 95
Author(s):  
Aristya Ardhitama ◽  
Rias Sholihah

INTISARI  Saat ini, kondisi cuaca di Pekanbaru dewasa ini begitu cepat perubahannya sehingga sulit diprediksi. Fenomena ini menuntut  prakiraan untuk meningkatkan kualitas hasil prakiraan sehingga lebih cepat, tepat, dan akurat untuk hasil yang diinginkan tersebut. Simulasi prakiraan jumlah curah hujan dengan menggunakan input data prediktor SOI, SST, Nino 3.4 dan IOD dengan parameter cuaca di Kota Pekanbaru telah  dilakukan menggunakan model persamaan regresi linear berganda. Prediktor tersebut digunakan untuk memprediksi curah hujan (CH) tahun 2011 dan 2012.Selain itu berfungsi untuk mengecek kebenaran hasil prakiraan jumlah curah hujan dengan model persamaan regresi linear berganda menggunakan rumus Root Mean Square Error (RMSE) dan Standar Deviasi (SD).Serta kajian penelitian ini berfungsi untuk membuktikan faktor prediktor (SOI, SST, Nina 3.4 dan IOD) yang paling mempengaruhi kondisi curah hujan di Pekanbaru.Data yang digunakan dalam kajian ini adalah data curah hujan sebaran normal dari tahun 1981-2010 pada stasiun wilayah Pekanbaru-Provinsi Riau. Data jumlah curah hujan tahun 2011 dan 2012 hasil observasi dianggap sebagai pembanding untuk verifikasi dan validasi nilai curah hujan (CH) hasil model output simulasi.Berdasarkan penelitian yang telah dilakukan maka dapat disimpulkan bahwa data dari SOI, SST, Nino 3.4 dan IOD memiliki pengaruh terhadap curah hujan di wilayah Pekanbaru Provinsi Riau.Kondisi cuaca terutama curah hujan untuk wilayah Pekanbaru dipengaruhi oleh factor global, regional dan lokal.Dari hasil penelitian terlihat hubungan yang memiliki tingkat korelasi yang tinggi terhadap curah hujan (CH) adalah prediktor SOI.Selain itu, dengan menggunakan RMSE membuktikan bahwa nilai kebenaran pada tahun 2011 lebih baik dibandingkan pada tahun 2012.  


2017 ◽  
Author(s):  
Martin Van Damme ◽  
Simon Whitburn ◽  
Lieven Clarisse ◽  
Cathy Clerbaux ◽  
Daniel Hurtmans ◽  
...  

Abstract. Recently, Whitburn et al. (2016) presented a neural network-based algorithm for retrieving atmospheric ammonia (NH3) columns from IASI satellite observations. In the past year, several improvements have been introduced and the resulting new baseline version, ANNI-NH3-v2, is documented here. One of the main changes to the algorithm is that separate neural networks were trained for land and sea observations, resulting in a better training performance for both groups. By reducing and transforming the input parameter space, performance is now also better for observations associated with favourable sounding conditions (i.e. enhanced thermal contrasts). Other changes relate to the introduction of a bias correction over sea and the treatment of the satellite zenith angle. In addition to these algorithmic changes, new recommendations for post-filtering the data and for averaging data in time or space are formulated. We also introduce a second dataset (ANNI-NH3-v2R-I) which relies on ERA-Interim ECMWF meteorological input data, along with built-in surface temperature, rather than the operationally provided Eumetsat IASI L2 data used for the standard near-real time version. The need for such a dataset emerged after a series of sharp discontinuities were identified in the NH3 timeseries, which could be traced back to incremental changes in the IASI L2 algorithms for temperature and clouds. The reanalysed dataset is coherent in time and can therefore be used to study trends. Furthermore, both datasets agree reasonably well in the mean on recent data, after the date when the IASI meteorological L2 version 6 became operational (30 September 2014).


Author(s):  
Alex Zepka ◽  
John Valadez ◽  
Parikshit Kulkarni ◽  
Kohei Yanagisawa ◽  
Kota Kobayashi ◽  
...  

2021 ◽  
Author(s):  
Vladimir S. Simankov ◽  
Pavel Yu. Buchatskiy ◽  
Andrey V. Shopin ◽  
Semen V. Teploukhov ◽  
Victoria V. Buchatskaya

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