bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fracture Face Damage: Diagnosis, Modeling, Solutions, and Study Cases in Ecuador
Authors: Izurieta Alvaro , Sierra Leopoldo 
Published: July 2020

Abstract
Underperforming fractured wells are often represented by fracture face damage or choked fracture models. This study shows a different pressure behavior identified from various historical cases in which mixed low- and high-mobility regions are present after hydraulic fracturing. Using available technology and processes to optimize the hydrocarbon production from wells in mature fields, this paper presents the data analyses and methodology used to analyze and discretize the induced damage after the stimulation process.
Production performance and pressure buildup data showed anomalous behavior in hydraulically fractured wells. To define this problem, an additional detailed reservoir simulation was performed to reproduce the damage mechanism that was not initially well identified by the pressure/derivative analysis. Various cases were examined to define the damage type, extent, and mobility changes present around the fractured area. Results from these simulations were used in pressure transient analysis software to generate analytical models that mimic the pressure behavior. The analytical models were compared with downhole data registered during well evaluation for validation.
Numerical models demonstrated that a low-mobility region was present around the fracture face and more consistent productivity results can be achieved by avoiding the creation of this region during fracture jobs by combining different breakers (delayed, temperature-activated, and fast reaction).
Pressure buildup data registered from different wells confirmed that the low-mobility region was not present.
Six Years Operating a Real Time Drilling Problem Detection Software in Deepwater Environments: Results and Challenges
Authors: Rodrigues G. da Silva Fabio , de Souza Cruz Marcelo , Barduchi Bruno , Bellumat Ernanda , Vieira dos Santos Maycon , Barroso de Matos Vinicius , Gandelman Roni Abensur , Leibsohn Martins André 
Published: July 2020

Abstract
Given the complexity and high costs associated to ultra deepwater drilling, any effort to avoid unforeseen situations is beneficial. Initiatives to optimize the drilling process in real-time are extremely important. This article details the experience of 6 year operating a real-time software with the focus of anticipating the detection of potential drilling problems in challenging scenarios.
Based on transient numerical simulation (hydraulics, cutting transport, torque and drag, etc.), the software automatically identifies operational problems in real-time, providing to the rig crew anticipated information, so that, corrective and/or preventive actions can be taken. The software incorporate relevant experimental and theoretical research developed in multiple Brazilian universities and R&D Centers. It receives surface and downhole data in real-time, as well as, simulates comprehensive models (which considers real data, fluid properties, well and drilling column geometry as inputs) in order to analyze the current drilling status, by comparing real and calculated (expected) data, with quantitative criteria. Dedicated decision trees, validated by a representative group of senior drillers, provide reliable indication for problem alerts.
The present paper aims to detail the successes and the difficulties of detecting typical drilling problems in Campos and Santos basins (offshore Brazil). The process of algorithm development, validation and field implementation is detailed.
In July 2014, after almost 10 years of development, the use of the application started as a service conducted by a drilling engineering team, on a 24x7 service in Petrobras Real Time Support Center. Since then, it monitored every Petrobras offshore well. According to estimates, the economy by using this real-time program reached almost 150 rig days, which is equivalent to approximately US$ 130 MM. In 2016, The iniciative was granted the Technological Innovation ANP (Brazilian National Petroleum Agency) Award as recognition by its relevant results. This software is in a continuous development process, aiming to acquire new analyses in order to increasingly help the Petrobras drilling operations.
The novelty is the usage of numerical simulation in order to withdraw the subjectivity of a qualitative data analysis, avoiding the misinterpretation of a possible operational event. Present challenges include the proposal of hybrid solutions (modeling + machine learning) to tackle the identification of complex problems (such as false kicks, stuck pipe in salt drilling and circulation loss prediction).
A Review on Fluids Technology for Near-Wellbore Remediation
Authors: Quintero Lirio 
Published: July 2020

Abstract
In the last two decades, the oil industry has dedicated considerable resources and efforts to developing chemical treatments to remove near-wellbore damage. The two main lines of work include formulations with multiple components (either solvent-based or water-based) and multifunctional microemulsion technologies that combine solvent-based and water-based treatments in a single-phase fluid.
Microemulsion technology has been applied in onshore and offshore wells, open-hole and cased-hole wells, newly drilled wells, and mature fields with issues of declining production. Various formulations are currently used for near-wellbore remediation in the oil industry.
This paper reviews publications relevant to near-wellbore remediation, in particular those that discuss microemulsion treatments. The review covers types of near-wellbore damage (emulsions, drilling fluids damage, sludge, scales, wettability alteration, paraffins and asphaltenes deposits) and the results of microemulsion evaluation and near-wellbore damage treatments.
The paper also presents a suite of laboratory tests for microemulsion evaluation and selection for near-wellbore remediation.
Machine Learning Applied to SRV Modeling, Fracture Characterization, Well Interference and Production Forecasting in Low Permeability Reservoirs
Authors: Urban-Rascon Edgar , Aguilera Roberto 
Published: July 2020

Abstract
The objective of this paper is to develop predictive models to optimize the (1) characterization of the stimulated reservoir volume (SRV), (2) discretization of the fracture network, and (3) hydraulic fracturing modeling, by combining machine learning (ML) algorithms and reservoir engineering in low permeability reservoirs.
An unsupervised learning algorithm is implemented to characterize the fracture network developed by micro-seismic observations during hydraulic fracturing. A Self Organizing Map (SOM) and Multi-Attribute Analysis are performed on the available seismic data to map the extension of the hydraulic fracturing stages and the fracture network complexity in a low permeability reservoir.
To correlate the mapped fracture network and discretized SRV, a 3D Finite Element Model (FEM) is developed to estimate fracture behavior, stress response, and hydraulic fracture propagation, on the predicted and forecasted multi-attribute map of the reservoir.
A 3D hydraulic fracture propagation model (HFPM) is introduced, to delimit the fracture geometry and remove data outliers in the SOM algorithm. Unsupervised algorithms rely on data quality. The efficiency of hydraulic fracturing modeling is improved with a machine learning approach by refining the certainty and quality of the data. An Artificial Neural Network (ANN) model helps to select the most significant parameters related to fracture modeling and simulation in the field. This approach allows us to recreate and forecast complex fracture networks in low permeability reservoirs, based on the learned geostatistical maps and hydraulic fracturing parameters, particularly where the microseismicity is limited or unavailable.
To validate the implementation of the 3D-HFPM in the field, an earthquake model is compared with statistically significant microseismic events obtained by the unsupervised iso-cluster algorithm. The relationship showed a good agreement, which suggests the HFPM agrees with seismic observations in the field.
The machine learning application to fracture network modeling provides the capability to identify susceptible areas to well interference and possible frac hits with higher certainty. This is so because the approach improves the selection of seismic data and hydraulic fracturing parameters, employed to develop the complex fracture network in numerical commercial reservoir simulators. This helps to determinate the reservoir interconnectivity and flow patterns in the fracture network.
This approach presents a robust manner for characterizing the SRV using a relative fast methodology, based on the combination of geostatistical and unsupervised learning modeling. The seismicity and hydraulic fracturing are connected using a multi-attribute and multi-disciplinary interpretation. It is a powerful tool for characterizing problematic fracture networks in unconventional reservoirs.
Heavy Oil in Ecuador: Pungarayacu Field, New Insights and Predictions for Development Strategy
Authors: Males Saverio Carlos Alfredo , Miranda Díaz Guillermo Javier , Palacios Serrano Diego Gabriel 
Published: July 2020

Abstract
Pungarayacu field is believed to has the largest heavy oil concentration in the Ecuadorian Oriente Basin. This study re-evaluates the Original Oil in Place (OOIP) and the potential reserves to be obtained by applying SAGD and steam flooding. The static and dynamic models were constructed considering new stratigraphic and sedimentological findings.
The proposed static model includes non-reservoir coaly facies obtained from density logs in order to generate a new petrophysical interpretation. Considering that some wells were drilled 30 to 40 years ago and the density logs were either reported in counts per second or non-existent, it was necessary to create a synthetic density log by Neural Network (NNT) for most of the wells in the northern part of Pungarayacu. A structural model was developed that represented the geological structure of the Hollin formation. Field is compartmentalized due to the presence of faults that generate several blocks with different WOCs (Water Oil Contacts). For building stratigraphic and sedimentological model, it was used stratigraphic correlations and previous depositional environments analysis of Upper Hollin sandstone (influenced by tides) and Lower Hollín sandstone (fluvial). For the petrophysical model the petrophysical properties distribution were based on probability maps and variograms. Pungarayacu oilfield has 8°API and a viscosity from 66,000 cp to 11,000,000 cp at 40°C showing severe oil biodegradation. Considering previous EOR screening analysis, the injection pilot of Cyclic Steam Stimulation developed by the last operator in the field and the quality of the sand; SAGD technique was applied in areas where the Hollin formation presents a low presence of shale and sandstone continuity; this allowed to develop a steam chamber for efficient oil drainage. In areas with shale intercalations, steam flooding was applied, because it allows lateral sweeping of the oil zone and improve the recovery factor. The new OOIP reported is approximately 21 billion barrels, from which 12.7 billion barrels can be produced by these two EOR thermal methods and the other 8.3 billion barrels are considered to be at depths shallower than 95 meters. In addition, the simulation results show that in 15 years the recovery factor will be 1.52 percent, equivalent to approximately 194 MMSTB, and in 32 years the recovery factor will be 3.15 percent equivalent to approximately 400 MMSTB.
Unexpected Low-Resistivity Pay Zone while Drilling a Horizontal Well: A Case Study from the Oriente Basin in Ecuador
Authors: Ameneiro Rosamary , Chiluiza Luis , Espinel Gavino , Guerrero Manuel Santiago , Ramirez Diana 
Published: July 2020

Abstract
Low-resistivity pay reservoirs have been described in many basins around the world. Technical information available explains the causes related to hydrocarbon bearing zones, represented by low resistivity values. These causes are mainly associated with mineralogy and complex pore structures. All the wells drilled and produced in the Johanna Este field in Ecuador have met the condition of pay zones with high resistivity values in relation to the well-known water formation resistivity.
A step change in the understanding of the main reservoir M1 sandstone occurred after drilling the horizontal well JE34H. The first half of the lateral section was drilled with resistivity values close to water resistivity. Vertical variations in the resistivity were confirmed in the second half of the well, with no lithologic barriers between both zones observed in the logs. The oil production obtained from this well was recognized as a field record, and the first time it was associated with low resistivity values.
After an exhaustive review of the available information including LWD electrical logs, the tool functioning, and cuttings description, it was possible to explain the low-resistivity pay within the M1 sandstone reservoir at the mineralogical level. The density-neutron cross-plots used for lithology identification show that the reservoir is mainly quartz with some mineral mixture. The target zone is mainly made up of medium-to-coarse grain with no visible matrix in the composition, showing no evidence of compaction or cement in density-neutron logs. Macroscopic anisotropy was also discarded as the reason for the decrease in the measured resistivity. In addition, structural compartments and preferential pathways for water invasion created by faults and/or fractures were discarded based on the azimuthal density image interpretation. The possible causes for the low resistivity pay were microporosity, presence of disseminated clay, and presence of conductive minerals. Special analysis (cores, magnetic resonance, capillary pressure, SEM) are required in order to understand the mineralogy and porosity distribution.
A low-resistivity pay within the M1 has opened a door for future analysis by means of conventional or special open hole and cased hole logs in order to identify reserves that could be bypassed.
Surface Facilities Development for Steam Flue Gas Injection in a Colombian Heavy Oil Reservoir
Authors: Perez Cordoba Jean Heli , Gonzalez Sanchez Edward David , Martinez Vertel Jaime Jose , Muñoz Navarro Samuel Fernando 
Published: July 2020

Abstract
Steam injection in heavy oil recovery often reaches the economical limit. Factors such as steam-oil ratio, generation costs, formation injectivity, reservoir pressure and heat losses are critical for the viability of these projects. This works presents a workflow to develop a scheme of surface facilities for different field conditions, that allows the capture, storage and usage of flue gas generated by steam generators and use it in a steam-flue gas injection process, which is going to mitigate the CO2 emissions, increase oil production and improve the results of steam injection alone in terms of energy efficiency.
The proposed workflow has 4 phases: 1) material balance to estimate the composition of flue gas generated based in generator parameters as gas feed, energy capacity and air excess; 2) numerical simulation of the involved processes as combustion, compression and heat exchange to study thermodynamic properties of the flue gas generated; 3) integrity analysis for equipment selection; and 4) determination of the most adequate scheme for flue gas managing according to field requirements.
Different case studies for surface facilities development are presented. High initial flue gas temperatures (400°F) and corrosion rates mainly by the presence of CO2 and O2 were identified as critical operation parameters. Since above 140°F corrosion rate gets higher compromising the integrity of the equipment, optimal relation between the gas feed used and an excess of air (15%) might allow a full combustion process decreasing the CO and hydrocarbons fraction in flue gas stream. Cooler units are required after the gas is compressed since the temperature raises, H2O separation has to be done after the first cooling process in order to mitigate the possible formation of carbonic acid on the stream; facilities dimensions are related to the capacity of steam generators and availability of flue gas volumes. The development of an adequate scheme of surface facilities requires an analysis of the conditions described before. Considering that each field has its own characteristics, the current workflow allows to develop optimal facilities for different projects using numerical simulation of multiple processes.
The methodology presented allows to determine the adequate facilities for different field conditions, in order to implement a steam flue gas enhanced oil recovery project that increases production and mitigates environmental impacts.
A Methodology to Identify the Root-Cause and Conditions of Sand Production, by Integrating Production, Reservoir and Completion Variables. Applied to a Colombian Field
Authors: Charry Torrado Anggie Paola , Vargas Silva Diego Armando , Mantilla Hernandez Hernan Darío , Quintero Peña Yair Andres 
Published: July 2020

Abstract
Sanding during hydrocarbon production is a critical problem in many fields around the world, in Colombia it is common in fields of the Magdalena Middle Valley, which entails high costs for operating companies due to continue cleaning and damaged equipment interventions, as well as, decrease in production. That is why predicting conditions under which sanding is being generated is an important component of field management. In this work a methodology has been applied that integrates into a diagnostic diagram: production information, sand production interventions, fluids, completion and formations, correlating the most critical sanding zones with the behavior of rock destabilizing variables such as: rates and fluid type (drag forces), liquid levels (drawdown impact) and rock competence.
From this analysis, it is possible to recognize the areas that are most sensitive to producing sand and the operating conditions that sharpen the scenario. For the field under study, Formation A was identified as the most prone to sanding due to the low resistance and degree of consolidation of its rocks, as well as the high water cuts and pressure depletion of the reservoir. Similarly, it was possible to identify drawdown management as a sanding-prevention measure, and the installation of Gravelpack as an exclusion method.
As a result of the state of the art carried out in support of this research, it was found that existing methodologies focus on estimating the stresses under which the rock fails and therefore produces sanding but they do not focus on analyzing the root cause and conditions that can cause sand production in the first place, which is important to understand the behavior of the rock under different stresses.
Quantification of Uncertainty in Model Based Type Well Construction
Authors: Ravikumar Arjun , Lee John 
Published: July 2020

Abstract
Production forecasting in unconventional reservoir systems is an important problem for the industry. While "averaging" observed production profiles is often used to construct type wells, model-based methods have significant advantages, including observance of basic fluid-flow principles. Model-based approaches for type well construction involve using analytical and numerical models for a representative well in a group of wells. Scaling factors are used to apply the model to the rest of the wells in the group. However, there is uncertainty in the estimates of these scaling factors that is not accounted for when we use point estimates. In this work, we calculate the uncertainty in the scaling factors in a group of analog wells. We then account for this uncertainty in the estimation of the P10, P50 and P90 type wells within the group of analog wells, using a Bayesian hierarchical model.
We select a group of analog wells and fit a model to a representative well in the group. We then calculate the scaling factor for each well in the group as a probability distribution. We pool these distributions to estimate the variation in scaling factors between different wells. This represents the uncertainty in the scaling factors across the group. We translate this into uncertainty in the production rate profile across the group. Finally, we generate P10, P50 and P90 production profiles from the distribution. These represent type wells for the group of analog wells.
We show that our technique is superior to point estimates of scaling factors traditionally used in the industry, due to pooling of distributions. Point estimates ignore possible variation in the scaling factor due to noise and presence of outliers. We show that this could accumulate and have a significant impact on the estimated type well. This propagates to the calculation of estimated ultimate recovery. We validate our method with simulated data, with and without added noise.
Forecasting production for producing wells and estimating production on proved undeveloped reserves are problems of engineering and economic importance. Existing methods using empirical averaging of production profiles or point estimates for scaling factors fail to use information on the uncertainty of the scaling factors. By discarding this information, we discard uncertainty that could influence reserves estimates. We present a method that incorporates this uncertainty into the analysis transparently. Our method can also be used to pool uncertainty across various well groups.
New Integrated Capillary Pressure and Initial Water Saturation Modeling
Authors: Liao Yizhu 
Published: July 2020

Abstract
This paper presents a new capillary pressure model, integrated and consistent capillary pressure modeling, and initial fluid saturation along wells and in reservoirs.
The new capillary pressure model is obtained by fitting Pc curves, end Pc values at end saturations, and threshold entrance Pc values satisfactorily. Analytical Pc derivative is then obtained. A consistent Pc derivative value can be used to obtain irreducible water saturation and corresponding maximum capillary pressure. After Pc curve fitting for multiple cores, correlations can be developed for the parameters in the new Pc model with different reservoir rock types and reservoir properties.
The new capillary pressure model is then used with free water level and reservoir fluid gravity to obtain the initial water saturation at any place in the reservoir. The initial water saturation vs. depth obtained dynamically from the new Pc model is integrated with that obtained statically from logs to achieve the integrated, reconciled, and consistent initial water saturation vs. depth.
Real examples are presented to show the step by step workflow procedure to obtain the new capillary pressure model and the integrated initial water saturation vs. depth. These examples include SCAL Pc curve data from cores and log Swi data vs. depth from wells in two offshore reservoirs.
This paper has three significant technical contributions. The new capillary pressure model is more versatile than those published and can handle Pc data while other existing Pc models cannot. The analytical Pc derivative from the new capillary pressure model can provide a consistent criterion for obtaining irreducible water saturation and maximum capillary pressure values for different rocks. The new capillary pressure model, when integrated with initial water saturation vs. depth from logs, can provide a consistent initial fluid saturation distribution in integrated static and dynamic reservoir modeling.