Groundwater depletion is an important problem driven by population growth, land use and land cover (LULC) change, climate change, and other factors. Groundwater depletion generates water stress and encourages unstainable resource use. The aim of this study is to determine how population growth, LULC change, and climate change relate to groundwater depletion in the Alto Atoyac sub-basin, Oaxaca, Mexico. Twenty-five years of dry season water table data from 1984 to 2009 are analyzed to examine annual groundwater depletion. Kriging is used to interpolate the region’s groundwater levels in a geographic information system (GIS) from mapped point measurements. An analysis of remotely sensed data revealed patterns of LULC change during a 34-year (1986–2018) period, using a supervised, machine-learning classification algorithm to calculate the changes in LULC. This analysis is shown to have an 85% accuracy. A global circulation model (GFDL-CM3) and the RCP4.5 and RCP8.5 scenarios were used to estimate the effects of climate change on the region’s groundwater. Estimates of evapotranspiration (using HELP3.5 code) and runoff (USDA-SCS-CN), were calculated. Since 1984, the region’s mean annual temperature has increased 1.79 °C and urban areas have increased at a rate of 2.3 km²/year. Population growth has increased water consumption by 97.93 × 10⁶ m³/year. The volume of groundwater is shrinking at a rate of 284.34 × 10⁶ m³/year, reflecting the extreme pressure on groundwater supply in the region. This research reveals the nature of the direct impacts that climate change, changing LULCs, and population growth have in the process of groundwater depletion.

In Neogene geologic settings, seismic resolution plays an important role in the static characterization of large stratigraphically complex reservoirs. Attenuation of high frequencies due to absorption and scattering of waves propagating through the subsurface contributes to the loss of seismic resolution, resulting, in particular, to a poor delineation of thin beds. This case history comprised the resolution enhancement of seismic data from one petroleum field located in the southern Gulf of Mexico. We applied discrete wavelet transform-based multiresolution analysis to identify frequency components in the data that required spectral enhancement. The resulting enhanced power spectrum contains a higher frequency content, which made it possible to identify new heavy oil (11°–23° API) reservoirs located in thin sandstones of deltaic sedimentary environments. The methodology used here helped us to cope with the attenuation problem.

A succesful petrophysical evaluation of shaly-sand formations requieres: 1) the availability of high quality well log data and, 2) a petrophysical model that successfully represents the geological conditions of the rocks. Unfortunately, it is not always possible to fulfill these conditions, and in many cases the set of well logs is incomplete. To determine petrophysical parameters (i.e., volumes of laminar, structural and disperse shale) in clastic rocks from incomplete well log data we followed three approaches which are based on a hierarchical model, and on a joint inversion technique: 1) Available well log data (excluding the incomplete well log) are used to train machine learning algorithms to generate a predictive model; 2) the first step of the second approach machine learning algorithms are used to generate the missing data which are subsequently included a joint inversion; 3) in the third approach, machine learning process is used to estimate the missing data which are subsequently included in the prediction of the petrophysical properties. The supervised learning paradigm we used was in a joint based on different regression models (linear, decision trees, and kernel). A performance analysis of the three approaches is conducted with synthetic data (representing real conditions of clastic formations from an oil field in southern Mexico). We simulated gamma ray, deep resistivity, P-wave travel time, bulk density and neutron porosity logs by means of a hierarchical petrophysical model for clastic rock to accomplish a controlled analysis. The three different approaches were applied without P-wave travel time data to analyze the impact of the missing information. In general, the results indicate an adequate petrophysical parameter determination in each of the approaches. Metric evaluations indicate that the best performance was obtained by the second approach followed by approaches one and three. The correct estimation of the volumes of shale distribution could not be correctly resolved by any of the three applied methods but the total shale content could accurately be predicted which suggests that there is a non-uniqueness problem.

We propose a new approach to improve spectral gamma-ray (SGR) logging forward modeling by considering the radioactive minerals present in the rock as gamma-ray sources. This is based on the radioactive attenuation theory. The assumptions are: 1) minerals with K40, U238, and Th232 content are considered radioactive sources uniformly distributed in the rock; 2) the measured radioactivity is proportional to the concentration of radioactive minerals and inversely proportional to the rock bulk density; 3) the radioactivity is only attenuated by absorption of gamma-rays. The forward modeling was tested using a synthetic case of sandstone with clay minerals and brine-saturated pores to analyze the sensitivity of SGR to changes in illite/smectite-illite/mica ratios and sandstone porosities. Finally, it was further validated with 44 core samples, of which 22 are from two shale gas and 22 from two clastic formations. The Pearson correlation coefficient applied to measure the misfit between the simulated and observed K, U, Th, and SGR data attained values of 0.82, 0.83, 0.61, and 0.57, respectively. A further improvement to 0.87, 0.85, 0.65, and 0.69 was achieved when applying joint inversion for data where illite/smectite and illite/mica ratios were not specified. The correlation between the simulated and observed data supports the viability of the proposed SGR forward modeling approach method. Proponemos un nuevo enfoque para mejorar el modelado directo del registro de rayos gamma espectral (SGR) al considerar los minerales radioactivos presentes en la roca como fuentes de rayos gamma. Este se basa en la teoría de la atenuación radiactiva. Los supuestos son: 1) los minerales con contenido de K40, U238, y Th232 son considerados fuentes radiactivas que están uniformemente distribuidas en la roca; 2) la radiactividad medida es proporcional a la concentración de minerales radiactivos e inversamente proporcional a la densidad aparente de la roca; 3) la radiactividad solo se atenúa por absorción de rayos gamma. El modelado directo fue probado usando un caso sintético de arenisca con minerales arcillosos y poros saturados con salmuera para analizar la sensibilidad de SGR a cambios en las relaciones de ilita/esmectita e ilita/mica y porosidades de la arenisca. Finalmente, el enfoque fue validado con 44 muestras de núcleo, siendo 22 de dos formaciones de gas en lutita y 22 de dos formaciones clásticas. El coeficiente de correlación de Pearson se aplicó para medir el desajuste entre los datos simulados y medidos de K, U, Th y SGR, obteniéndose valores de 0.82, 0.83, 0.61 y 0.57 respectivamente, y una mejora adicional de 0.87, 0.85, 0.65 y 0.69, respectivamente, fueron alcanzados aplicando inversión conjunta para los datos donde las relaciones ilita/esmectita e ilita/mica no fueron especificadas. La correlación lograda entre los datos simulados y observados sustenta la viabilidad del nuevo enfoque para el modelado directo propuesto de SGR.