Ledong HPHT gas field in Yinggehai basin of South China Sea. The pore pressure ramp above the over-pressured target sand with narrow safe mud weight window lead to high drilling risk. To improve drilling safety, multiple steps and approaches were taken in real-time monitoring of pore pressure and target zone depth prediction. This paper will describe the different techniques applied in this field to optimize mud weight and accurate prediction of casing shoe position. Using real-time LWD and mud logging data, pore pressure was calculated and geomechanics model was updated while drilling. The geomechanics model was built and calibrated against drilling and surface data. Intermediate vertical seismic profile (VSP) data was used to correlate with surface seismic profile to predict top of over-pressured reservoir and pore pressure below the bit. Last year, the latest look-ahead technology based on LWD deep electromagnetic measurement was also used to improve the depth prediction accuracy and thin bed detection for high-pressured sands below seismic resolution. The real-time pore pressure monitoring keeps the mud weight and equivalent circulating density (ECD) within a safe margin to avoid kick and mud loss. The estimated pore pressure coefficient calculated in real-time was within 0.01 SG from actual measurement taken in permeable sandstone intervals. VSP data provides formation top prediction between 50 to 200m ahead of the bit in this area. Predicted depth accuracy varies but generally stay below 10m for the depth range mentioned above. The integration of LWD and intermediate VSP logging has helped to reduce the high depth uncertainty in the predrill model. However, the above technique could not resolve for thin beds or low acoustic impedance contrast formation. This advocate to the use of real-time high-resolution inversion bed boundary detection ranging from 3 to 20m below the bit. Clear prediction of resistivity profile ahead of bit enable proactive decision making while drilling. This additional information helped to improve depth prediction accuracy for casing shoe position and ensure well integrity.

In December 1922, "Los Barrosos 2" gusher inserted Venezuela in the map of giant oil producers, joining an incipient industry that was to rule the world economy, but that still struggles in enabling the full participation of women, which precludes an appealing image of this industry for the female students of careers pertinent to oil and gas. The participation and roles of women in the oil industry experienced an evolution in the last two centuries, worth analyzing, as it provides key clues useful for the shaping of strategies related to diversity and inclusion programs in corporate frames. The applicability is evident for initiatives related to women, as the gender minority in the sector, but also for age, nationality, and different-ability minorities. This paper analyzes the evolution of specific roles of women in the oil industry and what elements propel their self-empowerment, grounding conclusions on a study case of Venezuelan women working in the oil industry from the 19 th to the 20 th century, in their home country and as part of the Venezuelan diaspora worldwide. The characteristics and main settings of the role of women in the oil industry have evolved substantially, and follow societal, legislation, cultural and unwritten rules or customary ways, that change in every region of the world. The Venezuelan case was selected, as the oil industry in their country underwent major changes, following social, political and legislation transformations that affected the sector. Three distinctive periods were established for the analysis: Early times until 1976 (industry nationalization) 1976 until 2000 After 2000 From the early years until the 70s, the role of women in the Venezuelan oil industry underwent major changes, from office-based and support roles to supervisory positions, in an era heavily driven by the presence of international oil companies in the country. The late 1970s through the early 2000s was an enlightening time, during which professional women in geosciences and engineering in Venezuela expanded the scope and outreach of their jobs, assuming and excelling in operational roles. As the 21st century progressed and the country's politics and economic stability deteriorated, many seasoned and young Venezuelan female geoscientists and engineers migrated abroad in search of new challenges and professional horizons. Additionally, the opening of societies everywhere inspired many of the new generations to seek jobs in other countries, in search of multicultural experiences. All these factors contributed to expanding the presence of Venezuelan women globally at an accelerated pace. How they adapted to new work settings along with the very different phases inside and outside their country of origin, continuing to succeed as an integral part of a diverse workforce worldwide, is not only remarkable but in many facets, unique. This paper presents specific observations and analysis about gender parity and roles of women about the leadership and participation women had in their Venezuelan home country and later on, in the global Venezuelan diaspora. We highlight some elements that we consider were key for the self-empowerment of women in Venezuela's oil sector. We expected to find several of these elements, as they are specific to the Venezuelan framework and culture, but others were findings worth sharing. Most relevant: education level, cultural admiration of the oil sector, societal perspectives on gender, respect for specialized knowledge, a cultural reverence of women who are breadwinners and sole heads of households, the "melting-pot" factor (integration of a varied, large and mixed migration as an integral part of society), Venezuelan legislation, and availability of multiple role models. The analysis of the role of Venezuelan women in academic and work sectors related to the oil industry is included, as applicable in Venezuela, showcasing the particularities in Engineering and Geosciences, in a period that spans more than a century, and that showcases gradual as well as step-changes in the participation of women in the oil and gas sector. The progress of female Venezuelan professionals working for the oil industry of their own country and abroad shapes a series of best practices for the inclusiveness of women, which we share because we think that how they did it and continue to do it, is replicable by other minority groups. Venezuelan women professionals have propelled and enhanced their organizations everywhere with quality and integrity, especially with their determination to conquer the future, high trust in their competencies, and a no-barriers attitude to overcome challenges.

This study aims to reduce uncertainties related to non-uniqueness in the interpretation of competing 4D effects and their impact to the interpretations and data assimilation into reservoir models. The study is based on synthetic and observed 4D seismic data, tracers, laboratory measurements, production and geophysical well log data. The methodology involves: (1) building and calibrating a petro-elastic model; (2) forward modeling of each separate physical effect such as saturation, pressure, salinity and noise; (3) combining the effects according to simulated production scenarios; and (4) correlating the modeled with observed 4D seismic data. We generate synthetic logs and seismic traces to quantify the time-lapse observations and to analyze how the combination of effects may affect the seismic character. This work demonstrates a major impact of competing 4D effects in the number and types of true possible interpretations. Yet, estimating their influence on the magnitude and polarity of 4D signal is still achievable. We show application examples from a Brazilian turbidite reservoir, in a setting where a combination of 4D effects occur simultaneously and the confidence level on the 4D interpretations is high due to good quality and frequent seismic data from a permanent reservoir monitoring system. In the example presented, a scenario where only water saturation effects occurs (e.g. aquifer water invading oil zone), 12 to 16% P-impedance (IP) increase (hardening effect) is expected. However, according to salinity measurements and tracers, injected sea water with lower salinity than the formation water decreases the hardening effect towards 8 to 10% in IP change. Having identified the impact of salinity changes in the 4D effects, an approach to include such changes in the petro-elastic model is proposed to generate 4D attributes that reproduce this interaction of effects. The outcome is a better match between modeled and observed 4D attributes, as compared to modeled attributes from a conventional petro-elastic model that considers salinity as constant. Additionally, combining these effects with gas going out from solution due to depletion below bubble point, the IP decreases significantly, reversing the polarity of 4D signal, and a 10% increase in gas saturation produces a softening effect (15% IP decrease). Competing 4D seismic effects are often mentioned but rarely quantified, and their resultant non-uniqueness impacts on similarity indicators used in data assimilation processes. As these similarity indicators are generally based on comparisons between observed and modeled seismic, the methodology presented results in an improved confidence on the workflows. Additionally, the methodology proposed is straightforward and adequate to reduce uncertainty related to 4D seismic interpretations.

The onshore Senoro field is an Indonesian carbonate pinnacle reef gas reservoir that so far has produced around 15% of the estimated original gas in place (OGIP). Twelve wells have been drilled consisting of ten production wells, one disposal well, and one monitoring well. All production wells were equipped with a permanent downhole gauge (PDG) to obtain near-bottom hole pressure data. A p/Z plot [ 1 ] of the pressure data exhibits a non-linear trend suggestive of pressure support from the aquifer or, perhaps, an adjacent gas reservoir in restricted communication with the main reservoir. If the non-linear trend is due to aquifer support, analysis assuming a weak water drive yields a bigger estimate of OGIP, and vice versa. Analysis of reservoir pressure and production data alone can result in unacceptably-high uncertainty related to drive mechanism and OGIP. This paper describes how cased-hole saturation logging from the gas reservoir across the contact and into the aquifer was integrated with other surveillance techniques based on pressure and production data to reduce the uncertainty and improve definition of drive mechanism and estimates of OGIP. Core data and pressure transient analysis in the Senoro field shows that the gas zone has a permeability in the range of 50 milidarcy (md) to 200 md, while the aquifer, which is mainly found in an underlying platform facies, has a much lower permeability in the range of 1 md to 40 md. The high permeability contrast between gas and water zone was expected to result in weak pressure support from the aquifer. Moreover, a tight streak is found in the middle of the aquifer which virtually eliminates bottom-drive aquifer support. Nevertheless, ongoing surveillance, including analysis of production data using flowing material balance and type curve analysis, indicates there is pressure support. Therefore, to address the uncertainty about aquifer support, it is necessary to evaluate the aquifer performance with other surveillance techniques to augment the PDG data. These other techniques included annual time-lapse saturation logging in the monitoring well, and static (wireline) bottomhole pressure (SBHP) surveys taken in select production wells and the monitoring well to estimate gas and aquifer pressure. After four years of production, the latest saturation log shows increased water saturation above the initial depth of the gas-water contact (GWC), indicating movement of the aquifer into the bottom of the gas column at the flank of the reservoir. The SBHP survey in the aquifer also shows that aquifer pressure has dropped by 330 psi compare to the initial pressure taken with a wireline formation tester. This compares to a pressure decrease of 370 psi in the overlying gas reservoir. Analysis of the current gas reservoir pressure trend, and the current aquifer trend, shows that the current depth of the GWC is about 50 feet shallower than the initial depth, which is consistent with the depth of the increased water saturation seen in saturation logging in the flank monitoring well. This result is consistent with the match from a simulation model with a weak water drive. Conversely, if a strong water drive is assumed, the decline in aquifer pressure will be smaller than observed and the GWC will be higher than observed. Sensitivity to combination of gas expansion energy and aquifer strength has been done to narrow the range of OGIP. The results described in this paper illustrate the benefit of integrating saturation and pressure measurements from the aquifer with other surveillance data to better measure aquifer performance, determine drive mechanism, estimate OGIP and recovery, forecast production, and anticipate problematic water production in a gas reservoir.

We use integrated micro-CT, thin section, XRF, SEM, and acoustic microscopy to quantify the controls on the acoustic properties of source rock reservoirs. At the smallest length scale a scanning acoustic microscope is used to measure velocities of individual laminae within unconventional samples. For unconventional reservoirs, in which typical particle and pore sizes are substantially smaller than 20 microns (i.e. the resolution of the acoustic microscope using a 20MHz probe), the difference in travel time between the first arrivals from the top and bottom surfaces of the sample provides an accurate measure of the velocity of each layer. The small feature size compared to the acoustic microscope wavelength eliminates the need for ray tracing. To upscale this data, one inch diameter core plugs are first micro-CT scanned and their acoustic properties are measured as received. After CT scanning, end trim and axial thin section and SEM mounts are prepared. The entire end trim, or 1" axial slice, is ion milled in preparation for SEM and acoustic microscopy. Large area image mosaics are produced using low voltage SE imaging for characterizing porosity, and BSE imaging for characterization of organic content and mineralogy. Scanning CL imaging and image analysis are utilized to differentiate between detrital and authigenic phases. Energy dispersive x-ray mapping is also used for the identification of major mineral phases. The resulting suite of mosaic images are analyzed using University of Houston developed image analysis software. Segmented volumes of porosity, TOC, and mineral phases are determined for each layer type in the sample. After the SEM imaging is complete, the velocity of each layer type is measured on the same sample volume using scanning acoustic microscopy. A Backus average of the measured velocities of each layer type agrees well with laboratory measurements made at the core plug scale for measurement performed perpendicular to bedding. We illustrate the correlations between segmented porosity, TOC, and mineralogy on the acoustic properties of each layer type. Mineral phases included in the modeling are clay minerals, pyrite, carbonate, and quartz. We include, where possible, the differentiation of authigenic quartz and detrital carbonate phases. Velocities for each layer type are mapped to the microCT data for the core plug. We illustrate the technique applied in several highly heterogeneous formations including the Niobrara, Haynesville, Barnett, Woodford, and Eagle Ford. As maturity increases, the location of organic material will shift from predominantly bedding plane parallel oriented laminae, to interparticle pores, as kerogen is thermally altered to bitumen. This results in distinct changes in the vertical acoustic velocity signal and in the observed anisotropy. By observing changes in acoustic velocity signals and relating them to bulk mineralogy, TOC and maturity, models can be calibrated to determine the presence and distribution of organic material.

In this paper, we developed an innovative machine learning (ML) method to determine salt structures directly from gravity data. Based on a U-net deep neural network, the method maps the gravity downward continuation volume directly to a salt body mask volume, which is easily interpretable for an exploration geophyisicist. We also studied the feasibility to apply the method to different gravity field data. We conclude that the ML based method from gravity data complements seismic data processing and interpretation for subsurface exploration. For the region where no or limited seismic data are available, this ML based salt identification can save iterative efforts (>50%) in the conventional gravity inversion process and identify major salt bodies in the region.

Reliable and representative models and detailed characterization of a geologically complex reservoir are crucially important in having better understanding of the reservoir behavior and directly guiding to the most efficient implementation of the field development plan. This study focused on developing models of the Upper Cretaceous Waha carbonate and Bahi sandstone reservoirs and the Cambrian-Ordovician Gargaf sandstone reservoir in the Meghil field, Sirte Basin, Libya. The goals of the study were to characterize the vertical and lateral spatial continuity of each of the three formations and to calculate deterministic and probabilistic volumetrics. The Meghil Field, discovered in 1959, is located on the Zelten Platform and regionally classified as an extensional area of the larger Zelten field. Nineteen wells were drilled targeting the primary reservoir interval of interest at a drilled depth of around 8000 feet. A 3D seismic program was conducted to develop detailed structural maps for the Kalash and Waha/Bahi/Gargaf formations to evaluate future gas field development program. The field is characterized by three slightly asymmetrical anticlinal traps trending NW-SE. Major and minor faults that cut the interior of the structure were detected in the seismic block. The available drill stem DST and production tests were used to evaluate the level of communication between the structures. The structural framework for a 3D reservoir model is based on the interpretation and integration of the seismic volume and the available well logs. The well log data show that the net hydrocarbon bearing zone thickness is about 270 feet, the average porosity ranges from 4% in the Bahi/Gargaf sandstone to 13% in the Waha limestone. The average water saturation ranges from 15% to 32% in the Waha limestone and the Bahi/Gargaf sandstone respectively. Geostatistical models were developed using the well log and core data along with the structural model developed from the 3D seismic volume. The models suggest that porosity decreases towards the flanks and that separate flow units are likely present. The deterministic and stochastic give estimates of the original gas in place of about 830 Bscf and 732.2 Bscf for the upper and lower reservoir intervals, respectively. This study demonstrated the potential for significant additional hydrocarbon production from the Meghil field as well as the potential impact of heterogeneity on well placement and spacing.

In the attempt to fill the scale gap between pulsed neutron and 4D seismic geophysical techniques to monitor underground reservoirs for ongoing oil/gas productionor CO 2 capture and storage, we introduce an emerging 3-axis borehole gravity technology that enables the recording of gravitational acceleration at high sensitivity, targeted at5 µGal. This is made possible using an innovation in resonant Microelectromechanical systems (MEMS) vibrating beam technology. This technology is designed to sense gravitational field produced by mass density changes in the subsurface, such that time-lapse wireline-based surveys may be taken to image fluid movements as far as 100s of meters from a wellbore and thereby enable time-lapse or 4D gravity monitoring. The innovation of 3-axis gravity measurement allows the acquisition of directional information about the spatial movement of fluid, even when acquired from just a single borehole.The cost effectiveness of a 4D wireline gravity survey compared to 4D seismic survey is highly attractive especially in the later stages of production surveillance programs, or as a complementary survey. We will first introduce the technology, and then present its application through a feasibility study aimed at the monitoring of CO 2 in a deep storage reservoir in Canada. We model and predict the gravity variation in a 4D gravity surveylikely to be seen due to density changes during a period of CO 2 injection at the storage site. Survey feasibility modelling and a workflow are presentedthat together provide important information forplanning and acquiring successful 4D gravity surveys, including the optimal time intervalbased on the planned injection rateand the optimal well location to use. The study will illustrate the general use for of time-lapse 3-axis gravity in monitoring reservoirs for optimising production over time while additionalexamples will be shown to further demonstrate application within oil/gas production reservoirs.

This study demonstrates the application of an interpretable (or explainable) machine learning workflow using surface drilling data to identify fracable, brittle and productive rock intervals along horizontal laterals in the Marcellus shale. The results are supported by a thorough model-agnostic interpretation of the input-output relationships to make the model explainable to users. The methodology described here can easily be generalized to real-time processing of surface drilling data for optimal landing of laterals, placing of fracture stages, optimizing production and minimizing frac hits. In practice, this information is rarely available in real-time and requires tedious and time-consuming processing of logs (including image logs), core, microseismic data and fiber optic sensor data to provide post-job validation of frac- and well-placement. Post-completion analyses are generally too late for corrective action leading to wells with a low probability of success and increasing risk of frac hits. Our workflow involves identifying geomechanical facies from core- and well-log data. We verify that the geomechanical facies derived using core- and well-log data have characteristically different brittleness, fracability and production characteristics. We test and investigate several different supervised classifiers to relate surface drilling data to the geomechanical facies. The data was divided into training and test datasets, with supervised classification techniques being able to accurately predict the geomechanical facies with 75% accuracy on the test dataset. The clusters predicted on test well (unseen data) were qualitatively verified using the microseismic interpretation. The use of Shapley Additive Explanations (SHAP) helps explain the predictive models, rank the importance of various inputs in the prediction of the facies and provides both local and global sensitivities. Our study demonstrates that pre-existing natural fracture networks control both the hydraulic fracture geometry as wells as the production. Natural fractures promote the formation of complex fracture networks with shorter half-lengths which increase well productivity while minimizing frac hits and neighboring well interactions. The natural fracture network is itself controlled by the geomechanical properties of the rock. The ability of the surface drilling data to reliably predict the geomechanical rock facies provides a powerful tool for real-time optimization of wellbore trajectory and completions.

In acid fracturing treatments, the goal is to create enough fracture roughness through differential acid etching on fracture walls such that the acid fracture can keep open and sustain a high enough acid fracture conductivity under the closure stress. The viscous fingering phenomenon has been utilized in acid fracturing treatments to enhance the differential acid etching. For relatively homogeneous carbonate reservoirs, by injecting a low-viscosity acid into a high-viscosity pad fluid during acid fracturing, the acid tends to form viscous fingers and etch fracture surfaces non-uniformly. In order to accurately predict the acid- fracture conductivity, a detailed description of the rough acid-fracture surfaces is required. In this paper, we developed a 3D acid transport model to compute the geometry of acid fracture for acid fracturing treatments with viscous fingering. The developed model couples the acid fluid flow, reactive transport and rock dissolution in the fracture. Our simulation results reproduced the acid viscous fingering phenomenon ob-served from experiments in the literature. During the process of acid viscous fingering, high-conductivity channels developed in the fingering regions. We performed parametric studies to investigate the effects of pad fluid viscosities and acid injection rates on acid fracture conductivity. We found that a higher viscosity pad fluid and a higher acid injection rate help acid to penetrate deeper in the fracture and result in a longer etched channel.

Leaks in wellbore tubulars emit acoustic waves in the borehole, which can be captured by a hydrophone array. Processing the array data yields location and energy level of the wellbore leaks; however, the hydrophones may also capture other coherent noise propagating as guided waves along the borehole, such as road noise caused by the displacement of logging equipment on the wellbore surface, by vibration from surface production facilities, and by distant loud leaks. Such coherent acoustic noise largely interferes with the estimation of a target leak's characteristics. Conventionally, to record higher-quality data, additional recordings may be required to station the tool at selected locations; however, this standard approach prolongs the acquisition time and constrains the vertical resolution. This paper describes an advanced approach to estimate and subsequently remove the guided-wave noise from the array hydrophone data to improve the accuracy of leak source locations. Furthermore, estimating the propagation direction and amplitude of leak-induced guided waves aids logging operations to efficiently locate a leak source. We propose an array data processing approach to separate the guided-wave noise from the direct arriving leak signal. The upward and downward propagating guided waves are identified using a least-square wave separation method. An alternative time-domain stacking method is also proposed for real-time fast computation. The slowness of the guided wave is optimized within a range and then used to estimate and remove the guided waves. Furthermore, characteristics of the estimated guided-wave noise can be extracted, such as the propagation direction, amplitude, and slowness. These parameters plotted in discrete depth locations provide additional information on the well condition. Synthetic and field data results show that the proposed method significantly improves the accuracy of the 2D flow map by removing the erroneous map artifacts due to guided waves. The low-frequency energy due to guided waves is also removed from the frequency spectrum log. The amplitude difference between the upward and downward propagating waves indicates the source direction of the leakage-induced waves. Hence, monitoring the guided wave amplitude and direction in real time provides an efficient way to quickly locate a leak source and reduce operation time. Conventional noise logging data are commonly contaminated by guided-wave noise. Single hydrophone tools cannot separate out these guided waves. The proposed approach, using a hydrophone array for separating guided-wave noise from direct arriving leak signals, provides a high-quality estimation of the energy and location of leaks in wellbore tubulars.

Hydraulic fracture height and width are key parameters for completion design and evaluation of unconventional resources. Traditional measurement technologies like microseismic, tilt-meters, chemical or radioactive tracers, pressure temperature gauges, etc. either have low resolution or rely on sensitive models which can cause a high degree of uncertainty. Recent frac-hit measurement methods include the use of Distributed Acoustic Sensing (DAS) deployed in far-field wellbores offset from the treatment well. The DAS system directly measures the fracture propagation every second through the completion, with a few meters' spatial resolution sampled at 0.25-meter intervals along the monitored fiber well. Cross-well fiber optic monitored far-field strain (FFS) results suggested elastic stress effects, intense inelastic fracture expansion and closure events which provide identification and measurement of frac height on a TVD plot, as well as width by measured depth along the wellbore laterals. Vertical wells or the heel-section of horizontal wells are suitable for frac-hit height (FHH) measurement; fracture azimuth and wellbore geometries need to be considered for precise evaluation. A specially designed engineered constellation fiber cable was tested and utilized in this method in combination with a true phase coherence DAS interrogator with a 20 dB improved sensitivity (Signal-to-Noise-Ratio (SNR)) for both low and high frequency ranges DAS. The optic fiber can be either permanently installed outside the casing or temporarily deployed inside a monitor well. Comparable results can be achieved by the engineered fiber system and have been presented within case studies for both horizontal and vertical well sections. In addition, Distributed Temperature Sensing (DTS) and data from downhole gauge can confirm any temperature or pressure changes resulting from frac driven interactions (FDI). With this approach, fracture azimuth, frac-hit corridor (FHC) width and FHH can be determined with a high degree of accuracy and resolution. Completion engineers were able to optimize frac models in real-time and further change completion schedules during the frac treatment.

Recently new Distributed Acoustic Sensing (DAS) data have been collected during hydraulic fracturing in shale. Low frequency DAS signals show patterns that are intuitively consistent with the understanding of the strain field around hydraulic fractures. This study utilizes a fracture simulator combined with a finite element solver to further understand the various patterns of the strain field caused by hydraulic fracturing. The results can serve as a "type-curve" template for the further interpretation of cross well strain field plots. Incorporating detailed pump schedule and frac fluid/proppant properties, we use a hydraulic fracture simulator to generate fracture geometries, which are then passed to a finite element (FE) solver as boundary conditions for elastic-static calculation of the strain field. Since the FE calculated strain is a tensor, it needs to be projected along the monitoring well trajectory to be comparable with the fiber strain, which is uniaxial. Moreover, the calculated strain field is transformed into time domain using constant fracture propagation velocity. Strain rate is further derived from the simulated strain field using differentiation along fracture length. Scenarios including a single planar hydraulic fracture, a single fracture with a discrete fracture network (DFN), and multiple planar hydraulic fractures, in both vertical and horizontal directions were studied. The scenarios can be differentiated in the strain patterns based on the finite element simulation results. In general, there is a tensile heart shaped zone in front of the propagating fracture tip. On the sides there are compressional zones parallel to the fracture. Multiple planar fracture show polarity reversals in horizontal fiber due to interactions between fractures. Strain field/strain rate show consistent patterns with what is observed from field cross well strain data. The application of the study is to provide a template to better interpret hydraulic fracture characteristics using low frequency fiber strain monitoring. To the author's understanding, there are no comprehensive templates for engineers to understand the strain signals from cross well fiber monitoring. The results of this study will guide engineers toward better optimization of well spacing and frac design to minimize well interference and improve efficiency.

Knowledge of pore pressure, in-situ stress, and lithology in unconventional reservoirs is important for safe and economic drilling, hydrocarbon production, and geomechanics applications such as wellbore stability analysis and hydraulic fracturing. Reliable predrill predictions of pore pressure, in-situ stress, and lithology are thus required for safe drilling and optimal development in such reservoirs. In the Permian Basin, changes in lithology occur over vertical depths that cannot be resolved by seismic velocities obtained by kinematic analysis, as these have poor vertical resolution. To obtain improved vertical resolution, seismic prestack depth-migrated (PSDM) data are input to amplitude variation with offset (AVO) inversion, for an area in the Delaware Basin where wide-offset 3D seismic data are available. AVO inversion provides estimates of both P- and S-impedance. The results are used to build a 3D mechanical earth model, which is employed to predict pore pressure, in-situ stress, and geomechanical properties. The model enables integrating the results of seismic inversion with drilling data, measurements on cores, wireline logs, formation and fracture closure pressures, and other data. By employing P- and S-impedance, and their ratio, pore pressure, in-situ stress, and lithology derived from seismic prestack inversion provides greater resolution than estimates obtained using seismic velocities from kinematic analysis. Examples from the Permian Basin illustrate the importance of the results for unconventional reservoir development.

Unexpected problems during completion create costs that can cause a well to be outside its planned AFE and even uneconomic. These problems range from merely experiencing abnormally high pressures during treatment to casing failures. The authors of this paper use machine learning methods combined with geomechanical, wellbore trajectory, and completion datasets to develop models that predict which stages will experience difficulties during completion. The operator collected geomechanical data for 26 lateral wells. Several of these wells experienced significant difficulties during completion, including casing failures. By examining the completion data for the study wells, engineers developed two objective proxies for trouble stages: a high fracture gradient cut-off and a sand-to-water ratio cut off. The geomechanical data were combined with wellbore trajectory and stage-level completion data. Machine learning techniques were then used to predict the fracture gradient at the end of treatment and the sand-to-water ratio of a stage. The data were subdivided by target horizon to ensure the model results were not skewed towards a specific horizon. The models were cross validated, hyper-parameter tuned, and then tested against a subset of the data withheld from the training algorithm (the test data subset). Multiple metrics were applied to measure the effectiveness of the models. For the fracture gradient regression, the model that minimized the root mean squared error (RMSE) was chosen. This model was compared to a prediction that each stage would have a fracture gradient equal to the average fracture gradient. The correlation between predicted and measured frac gradients was also calculated. These metrics were analyzed for both the entire data set and the test data subset. By all these measures the model performs significantly better than the control prediction. For the sand-to-water ratio metric, the model that maximized the receiver operating characteristic area under the curve (ROC-AUC or AUC) score was chosen. We chose to measure the success of the model with the precision of its prediction of outlier (trouble) stages on the test data subset. The model performs with a precision of at least 73%. Both models identify stages where casing failures occurred as trouble stages. With these initial results, the authors are confident they can predict the probability of any stage causing a significant operational disruption in these horizons. By predicting the probability of a trouble stage, mitigation plans can be implemented to reduce costs ahead of time. By further analyzing the models, engineers can identify the major drivers for the trouble stages and work to reduce them as early as the well planning stage.

Stage length and perforation cluster spacing are important design parameters for multi-stage hydraulic fracturing. This study aims to demonstrate that the interplay between subtle variations of the least principal stress (S hmin ) with depth and the stress shadows induced by simultaneously propagating hydraulic fractures from multiple perforation clusters, primarily determines the propped and fractured area in the target formations. This principle is illustrated with the help of a case study in a prolific unconventional formation in the north eastern US, where the vertical stress variations are well characterized through discrete multi-depth stress measurements and actual stage design parameters used by the operator are known. At first, we show how the hydraulic fracture footprint and proppant distribution varies with a change in the vertical stress profile. The stress profile is shown to be a very important in determining the optimal vertical and lateral well spacing. The evolution of the stress shadow in the different layers is shown during the pumping as the fracture propagates across multiple layer boundaries. Subsequently, we demonstrate that by changing the magnitude of stress perturbations caused by the stress shadow effect, the distribution of propped area can be altered significantly. We use this method to determine the optimal cluster spacing keeping other design parameters constant such as flow rate, perforation diameter, etc. Simulations from selected cluster spacing realizations are run with high and low permeability scenarios to show the importance of correct matrix permeability inputs in determining the three-dimensional depletion profile and ultimate production. By varying the cluster spacing we show the hydraulic fracture propagation change from being solely stress layering driven to stress shadow influenced. The effect of stress shadow on the final fracture footprint is highly specific depending on the given stress layering and is thus case-dependent. This study demonstrates that knowledge of stress variations with depth and modeling are critical for optimizing stimulation efficiency.

Diffusive-Time-of-Flight (DTOF), representing the travel time of pressure front propagation, has found many applications in unconventional reservoir performance analysis. The computation of DTOF typically involves upwind finite difference of the Eikonal equation and solution using the fast marching method (FMM). However, application of the finite difference based FMM to irregular grid systems remains a challenge. In this paper, we present a novel and robust method for solving the Eikonal equation using finite volume discretization and the FMM. The finite volume form of the Eikonal equation is derived by differential manipulation, volume integration and the divergence theorem. Using product rule, the differential term is first converted to the divergence form. Then volume integrals that contain divergence terms are converted to surface integrals using the divergence theorem. Consequently, the spatial coordinates are replaced by cell volumes and transmissibilities which are universal for both structured and unstructured grids in finite volume simulators. When applied with the upstream scheme, the finite volume form evolves into a set of quadratic equations, and fast marching method is implemented to solve these equations. The implementation is first validated with analytical solutions using isotropic and anisotropic models with homogeneous reservoir properties. Consistent DTOF distributions are obtained between the proposed approach and the analytical solutions. Next, the implementation is applied to unconventional reservoirs with hydraulic and natural fractures. Our approach relies on cell volumes and connections (transmissibilities) rather than the grid geometry, and thus can be easily applied to complex grid systems. For illustrative purposes, we present applications of the proposed method to embedded discrete fracture models (EDFM), dual-porosity dual-permeability models, and unstructured PEBI grids with heterogeneous reservoir properties. Visualization of the DTOF provides flow diagnostics such as evolution of the drainage volume of the wells and well interactions. The novelty of the proposed approach is its broad applicability to arbitrary grid systems and ease of implementation in commercial reservoir simulators. This makes the approach well-suited for field applications with complex grid geometry and complex well architecture.

Computation of seismic-wave traveltimes is used in seismic imaging procedures such as Kirchhoff migration. For realistic applications, one has to precompute large traveltime tables (for all sources, receivers, and imaging points). This implies massive computations as well as storage of large files with these traveltime tables. One of the popular traveltime computation methods is a numerical solution of the eikonal equation. In this paper, I addressed the idea of using artificial neural networks for optimizing traveltime computations and using traveltimes in Kirchhoff migration. First, I used supervised learning for approximating and compressing the traveltime tables by artificial neural networks. Second, I used unsupervised learning for solving the eikonal equation. I used fully-connected neural networks for solving both problems. For the first problem, I used traveltimes precomputed on a coarse for supervised training of a neural network. Synthetic tests show that this neural-network approximation provides great compression of the traveltime tables (10 2 −10 5 times) with reasonable accuracy of predicting traveltimes on a fine imaging grid. Overall, the use of artificial neural networks results in a speed-up of the Kirchhoff migration operator in two applications: microseismic event localization (by three times) and reflection-seismic migration (by four times). The second problem was to use artificial neural networks for solving the eikonal equation. The main result was a special design of a loss function that ensures solution of the eikonal equation and allows for neural-network unsupervised training. In the synthetic test, the neural network was successfully used for solving the eikonal equation (forward problem) with slightly better accuracy compared to the first-order Fast Sweeping Method. I also demonstrated that neural networks could also solve the inverse problem – back propagate traveltimes from the observation surface into the subsurface. Such inversion was illustrated by successfully solving the problem of microseismic event localization.

Many investigations have been discussed and it is a well-recognized fact that sonic wave velocity is not only influenced by its rock matrix and the fluids occupying the pores but also by the pore architecture details of the rock bulk. This situation still brings a lack of understanding, and this study is purposed to clearly explain how acoustic velocity and quality factor correlate with porosity, permeability and details internal pore structure in porous rocks. This study employs 67 sandstone and 120 carbonate core samples collected from several countries in Europe, Australia, Asia, and USA. The measured values are available for porosity ϕ , permeability k , clay content V cl , compressional velocity V p, and quality factor Q p in saturated and pressurized conditions. Then, a proposed method is developed by re-arrangement on Kozeny equation to perform rock typing based on pore structure similarity which called as pore geometry-structure (PGS). The proposed rock typing method allows investigating the influential primary factors that control acoustic velocity and quality factor. Besides that, basic rock physics equations for sonic velocity and critical porosity concepts are also involved and derived to obtain a new solution to predict porosity and permeability. At least eight rock groups are established from rock typing with its Kozeny constant. This constant is a product of pore shape factor Fs and tortuosity τ . Then, the relations of velocity and quality factor versus porosity, permeability, pore geometry ( k / ϕ ) 0.5 , and pore structure ( k / ϕ 3 ) are constructed. One can find that each relation among the rock groups of each lithology is clearly separated and produce high correlations. Velocity and quality factor tend to be high with an increase in Kozeny constant. However, for a given porosity for all the groups, velocity and quality factor increase remarkably with a decrease in Kozeny constant. These all mean that velocity and quality factor increase with either an increase in the complexity of pore systems or, at the same pore complexity, a decrease in specific internal surface area. On the other hand, each rock group for both sandstone and carbonate has its critical porosity and it strongly correlates with velocity and porosity. Finally, critical porosity becomes a specific property of rock groups having similar pore geometry and structure. As a novelty, the empirical equations are derived to estimate compressional velocity and quality factor based on petrophysical parameters. Furthermore, this study also establishes empirical equations for predicting porosity and permeability by using compressional wave velocity, critical porosity, and PGS rock typing.

Enhanced oil recovery (EOR) is an economic way of producing the remaining oil out of previously produced Devonian Pinnacle Reefs in the Nisku Formation within the Bigoray area of Alberta. To maximize the recovery factor of the remaining oil, it was necessary to first characterize the geological structure, matrix reservoir properties, vugular porosity and the natural fracture network of these two carbonate reefs. This characterization model was then used for reservoir simulation history matching and production forecasting further discussed by ( Rivero, 2019 ). With the enhanced resolution of a reprocessed 3D seismic volume, more accurate seismic interpretation was completed to better delineate the internal and external structure of the reefs. The petrophysical analysis and core interpretation showed that the two reefs could be divided into two zones; the bottom zone has low porosity and the upper zone has high porosity that was targeted in previous well completion schemes. These zones were easily picked on well logs and when using Seismic Ant Tracking attributes, were accurately interpreted within the seismic volume. With the framework of the geomodel developed, rock type, porosity, permeability and water saturation were interpolated within the reservoir. Because natural fractures in these carbonate reservoirs are known to be an important part of fluid movement, it was important to characterize the discrete fracture network. In one well, a borehole image successfully quantified the properties of the natural fracture network. The observed fracture density (5 fractures/m) suggested discreate fracture zones throughout the well which was also confirmed with core fracture mapping. As part of the geomodel, a discrete fracture model (DFN) was generated; Seismic Ant Tracking was used to interpolate the fracture intensity within the reservoir. In these Devonian Pinnacle Reefs, and in other reservoirs, before investing in an EOR scheme, it is critical for the operator to understand the geologic structure and the petrophysical characteristics of the reservoir in as much detail as possible. This paper demonstrates how log and seismic data that is up to 40 years old can be converted to modern data types and be used to characterize a reservoir in a way not possible before.