bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Comprehensive Techno-Economic-Assessment of Alkali-Surfactant-Polymer Flooding Projects Using Data-Driven Approaches
Authors: Sun Qian , Ertekin Turgay , Zhang Miao , On Thinh 
Published: October 2020

Abstract
The objective of this paper is to structure a data-driven-based workflow to design and optimize alkali-surfactant-polymer flooding (ASP) projects. Various objective functions from the techno-eco perspectives are considered to obtain comprehensive optimum ASP slug formulations and injection operation schemes. Several universal multi-layer neural networks (MLNN) are trained and they act as surrogate models of high-fidelity numerical simulation models to evaluate the objective functions involved in the optimization workflow. The input parameters considered by the machine-learning workflow include the reservoir rock and fluid properties, ASP slug formulation, and project design parameters. A physical equation of state, HLD-NAC equation, is employed to model the microemulsion phase behavior of the crude oil/brine/surfactant system. The validity of the surrogate models is confirmed via extensive blind testing applications with error margins of less than 5%. Thus, the MLNN can be employed as an expert system to assess the response functions of the ASP injection processes, including oil recovery, water cut reduction, pressure responses, etc. A hybrid global optimization workflow is structured by coupling the expert MLNN systems with particle swarm optimizer (PSO). Comprehensive techno-eco-assessments are carried considering the oil recovery, chemical slug utilization ratio (incremental oil production per unit mass of chemical additives), and project economics as objectives. This paper presents case studies to illustrate the robustness of the proposed workflow in optimizing the ASP injection projects considering multiple objective functions. The optimization work focuses on the design of ASP slug formulations and injection patterns. A sensitivity analysis is carried to investigate the trade-off factors between pairs of the objectives. Objective functions exhibiting a strong trade-off relationship can be included to structure Pareto front solutions, which enables the optimization workflow to find various optimized ASP formulations and injection schemes. The outcomes of this work not only provide field operators with various options to implement ASP injection processes but also generate stochastic assessments of the optimized objectives. This work develops a universal proxy model that is competent to generalize the microemulsion phase behavior that occurs in the ASP injection processes. The trade-off relationship amongst the critical objective functions is investigated via a rigorous sensitivity analysis using the proxy models. With the help of the developed model, the decision-makers can design ASP injection projects based on various project preferences. Moreover, the project risks can be understood by observing the stochastic outcomes of the optimized objective functions.
Pump-Down Diagnostics for Plug-and-Perf Treatments
Authors: Cramer David , Snyder Jon , Zhang Junjing 
Published: October 2020

Abstract
This paper introduces pump-down diagnostics, an economical process in which cement sheath integrity, perforation cluster spacing and frac plug integrity can be assessed for every frac stage, potentially leading to improvements in stimulation, completion, cementing and drilling practices. It is based on analyzing wellbore pressure responses occurring at key segments of the wireline pump-down and perforating operation and correlating the results among multiple frac stages and wells in a field or play. A special requirement is that the frac ball (ball check) is inserted in the frac plug and pumped to seat prior to performing perforating operations. A complementary benefit of this process is that selectively establishing injectivity in the most distant perforation cluster can be used to establish inhibited HCl acid (wireline acid) coverage across all perforation intervals for uniform reduction in near-wellbore tortuosity.
Reviews of pump-down diagnostics field cases from several unconventional plays provide the following insights. 1.) pump-down diagnostics are time efficient and economical, requiring about 15 minutes per frac stage, 2.) evaluating communication to the previous frac stage can serve as a key performance indicator for treatment control or cement sheath integrity, 3.) pump-down diagnostic results can be more reliable than cement bond log evaluation, 4.) stage isolation characteristics can be strongly affected by cluster spacing.
Effective Method to Reduce Circulating Pressures in A Pressurized Mud Cap Drilling System: A Case History of Friction Reducer in Circulating Fluid
Authors: Samuel Nishanth , Dewbre Kyle , Landis Charlie 
Published: October 2020

Abstract
An application for Pressurized Mud Cap Drilling (PMCD) was needed to successfully drill an U.S. onshore, unconventional, High Pressure / High Temperature (HP/HT) well. The length of the lateral, highly fractured formation & extremely high MWs needed to drill the lateral proved that conventional drilling methods would not be effective, so PMCD was utilized. The main challenge was to keep circulating pressures low while injecting the circulating fluid into the naturally highly pressurized formation. PMCD involves injecting / losing the circulating fluid (water) into the formation, in this situation, high formation fracture pressures required high surface injection pressures to push the fluid & cuttings into the formation. When the injection pressure was combined with the frictional losses through the drill string, anticipated surface pressures were over 10k psi when circulating. To combat the high circulating pressure, an approach was taken to use Friction Reducer (FR) in the circulating fluid, water, thus reducing the frictional pressure loss while circulating within manageable constraints. This paper will go into detail of the different phases taken to determine the appropriate type of FR based on lab testing, hydraulics model, temperature modeling, and the results from the application of the product in the drilled well. Extensive hydraulics modeling was conducted to determine the feasibility of using the FR and the final anticipated circulating pressure. The BHA and drill string design was optimized to maximize the least amount of pressure drop across the tools while staying within operating parameters. Concerns of the use of FR such as ECD effect, fluid flow characteristics for hole cleaning in the annulus, temperature effects of the water being pumped, and effects in the BHA were all considered in the engineering process. FR testing including, lab testing of the base fluid (water), differential scanning calorimeter to determine effects of BHT, FR shear testing, FR concentration percentage at various flow rates in a flow loop to calculate Reynolds number at the respective flow rates & concentration and differential pressure testing with time and temperatures. Based on the results from the hydraulics modeling and lab tests, an FR was selected and fully utilized in the application of the product. PMCD techniques with this FR was used and was fully successful in the intended reduction of the surface circulating pressures while drilling the well. Surface and downhole results captured the effects of the FR in the fluid system and data was collected and analyzed to show the expected vs actual results on various drilling parameters.
Hydrocarbon Index Identification in Lateral Section of Horizontal Wells Using Machine Learning
Authors: Toktarov Max , Aun Siddiqui Mohammed , Permata Ilham , Khakimov Sattor 
Published: October 2020

Abstract
Machine learning is a powerful tool that has become an essential part of the oil and gas industry that provides additional insights at limited data availability. In the vertical section, representative samples can be obtained, but in the horizontal section, core and well log data are extremely rare. With machine learning, mineralogy data can be trained to predict Total Organic Conten (TOC) and amount of free hydrocarbons (S1) and implemented in the lateral section of a well. X-Ray Diffraction (XRD) data were used to train the Machine Learning Model to predict TOC & Flowable Hydrocarbon Index (FHI). For training, the vertical section of a well along with data from other wells nearby was used. It was then tested on the horizontal section of the same well to make certain that the model had learned meaningful relationships. The data were pre-processed to remove outliers, clean string variables, and missing values. New features were then added using domain knowledge, as some features are non linearly correlated with TOC. For example, certain minerals correlate with TOC and S1 differently in different flow units. Because of this, we made sure to collect data for training and testing within the same flow unit. The features were then standardized to ensure that the resulting distribution had a mean of 0 and a standard deviation of 1. This was done for two reasons, firstly, it makes sure the Machine Learning algorithm gives equal weightage to all features irrespective of their units, and range of values. Secondly, it helps the algorithm to converge faster, taking less time to train. The data was then fed to the model to train on. Once the model was trained, the hyperparameters of the model were then tuned using a suitable error metric. This was done in order to make certain that the model performs well on test data, and does not overfit on the training data. Results of the trained model were compared to actual measured results from the rock along the horizontal wellbore. The predicted and measured results show a great match based on several metrics such as coefficient of determination, mean absolute error, mean squared error, and root mean squared error. The key observation was the importance of the sample acquisition, sample handling, eliminating human error during measurements in the lab, and data handling when applying the model. In conclusion, when the data is properly clustered using geological understanding it is meaningful to apply machine learning algorithms to find multivariate relationships among different parameters. This paper presents a novel approach of obtaining volumetrics data based on "ground truth" measurements such as saturations along the horizontal wellbore without operational risks, potential downtime, and chances to lose tools.
Diffusion-Based Modeling of Gas Transport in Organic-Rich Ultratight Reservoirs
Authors: Liu Zizhong , Emami-Meybodi Hamid 
Published: October 2020

Abstract
The complex pore structure and storage mechanism of shales make the mass transport in these ultratight reservoirs complicated and significantly different from typical conventional rocks. A substantial fraction of total pore volume in organic-rich ultratight reservoirs consists of nanopores in which the notion of viscous flow may become irrelevant. Instead, multiple transport and storage mechanisms should be considered to model fluid transport within the shale matrix, including molecular diffusion, Knudsen diffusion, surface diffusion, and sorption. This paper presents a diffusion-based semi-analytical model for a single-component gas transport within an infinite-acting organic-rich ultratight matrix. The model treats free and sorbed gas as two phases coexisting in nanopores. The overall mass conservation equation for both phases is transformed into one governing equation solely based on the concentration (density) of the free-phase. As a result, the partial differential equation (PDE) governing the overall mass transport carries two newly-defined nonlinear terms; namely, overall diffusivity, D, and sorption-corrected porosity, Φ. The D term accounts for the molecular, Knudsen, and surface diffusivity, and the Φ term considers the mass exchange between free- and sorbed-phases under sorption equilibrium condition. Both D and Φ are functions of free-phase concentration. The nonlinear PDE is solved by applying a piecewise-constant-coefficient technique that divides the domain under consideration into an arbitrary number of subdomains. Each subdomain is assigned with a constant D and Φ. The diffusion-based model is validated against numerical simulation. The model is then used to investigate the impact of surface diffusivity, Knudsen diffusivity, porosity, and adsorption capacity on gas transport within the ultratight formation. Further, the model is utilized to study gas transport and production from Barnett, Marcellus, and New Albany shales. The results show that surface diffusion significantly contributes to gas production in shales with high surface diffusivity and adsorption capacity and when the Knudsen diffusivity and total porosity are small. Thus, neglecting surface diffusion in organic-rich shales may result in the underestimation of gas production.
Improving Profile Control by Deriving Pore-Throat Size and Volume of Thief Zones from the Variation and Derivative of Water Cut
Authors: He Yifan , Chen Jianbo , Shi Xinlei , Sun Qiang , Xu Yanan 
Published: October 2020

Abstract
After long-time water injection, thief zones (TZ) tend to form and reduce the effectiveness of water flooding in mature oil fields. Several profile control techniques have been developed to resolve this problem. The effect of profile control depends on quantitative calculation of pore-throat size and volume of the thief zones which determines the most critical treatment factors such as the injection particle size and slug volume. Tracer tests or ‘PI’ index were normally used to identify thief zones. However, tracer tests are time-consuming and high-cost, especially for offshore fields; ‘PI’ index only reveals a relatively higher permeability area but not necessarily indicate presence of thief zones.
This paper presents a new method to identify TZ as well as pore-throat sizes and volumes calculation by using the variation and derivative of water cut. The procedures are as following:
Two theoretical models of injection and production were established to simulate water flooding development by stream tube method. The first model is a standard model and includes only normal reservoirs. The second model is a comparison model with presence of both high permeability zones and normal permeability zones. The water cut of two models was compared to distinguish the key points and curve features of TZ models. The key points and derivative curve were used to identify the existence of TZ. The ratio of high permeability zones to normal permeability zones as well as the absolute permeability, width and height of TZ were figured out using the peak values and their arrival times on the derivative curve of water cut. Calculate pore-throat radius and porosity of TZ using the absolute permeability in step 3 based on mercury injection capillary pressure (MICP) and then the pore volume of the TZ was determined.
In a word, the fluctuation of the water cut curve and the multi-peak values of the water cut derivative curve are due to the fact that the injected water first reaches the production well along the high permeability layer of TZ, and then reaches the production well along the normal reservoir. Furthermore, larger pore volume of TZ will have more influence on water cut value and a larger water cut will be seen when the second peak value occurs on water cut derivative curve.
This workflow had been applied to QHD32-6 oilfield for the profile control treatment design of over 10 well-groups were planned and achieved commendable results: the maximum water cut reduction reached 8% and the oil production noticeable increased by 300bbls per day with an output ratio over 5:1.
CO2 Foam Field Pilot Monitoring Using Transient Pressure Measurements
Authors: Karakas Metin , Alcorn Zachary Paul , Graue Arne 
Published: October 2020

Abstract
This paper presents the analysis of transient pressure measurements from a recent CO2 foam pilot in East Seminole Field, Permian Basin, USA. A surfactant-stabilized foam was selected to mitigate CO2 EOR challenges in this field by reducing CO2 mobility in an effort toimprove sweep efficiency, oil recovery, and CO2 storage potential. The surfactant system was designed in the laboratory by measuring surfactant adsorption and verifying foam stability. A surfactant-alternating-gas (SAG) injection strategy, with 10 days of surfactant solution followed by 20 days of CO2, began in May 2019. The pilot monitoring program aimed to evaluate reservoir response to foam injection. Surveys included CO2 injection profiles, CO2 tracer tests, collection of injection bottom hole pressure/temperature data, and three-phase flow rates.
Injection BHP and temperature data from the downhole pressure gauge (DHPG) was used to evaluate the pilot response during surfactant and CO2 injection. The analysis was conducted by examining the differential pressure (dP) and differential temperature (dT) through time for the first nine SAG cycles. A high-resolution two-dimensional radial flow model was developed to history match the measured transient pressure data. The simulation model included the porosity and permeability distribution from a validated sector-scale model of the pilot pattern and surrounding producers. The radial flow model was used to examine the impact of foam and/or relative permeability on injectivity and mobility reduction when switching between surfactant solution and CO2 in a SAG process.
Transient analysis showed that the temperature responses were quite similar during most SAG cycles. On the other hand, differential pressures consistently increased during periods of surfactant injection and decreased during the subsequent CO2 injection periods. The pressure increase (buildup) during surfactant injection was due to a decrease in mobility, showing development of a mobility bank in the reservoir. There are also questions regarding the impact of foam and/or relative permeability on injectivity and mobility reduction when switching between surfactant solution and CO2 in a SAG process.
Reservoir Surveillance Adds Values to LCI Waterflood Oil Field in Colombia
Authors: Yao Calvin C. , Rueda Armando , Bernal Maria C. , Bastidas Lievneth , Blanco William F. , Rueda Luz A. , Duran Andrea C. 
Published: October 2020

Abstract
The La Cira-Infantas (LCI) oilfield was discovered and put in production in 1918. After a traditional reservoir depletion stage, a water flood (WF) operation was started in 1958. Redevelopment began in 2005 as a joint venture between Ecopetrol and Occidental (Oxy) using a combination of workovers, infill drilling, WF optimization, and other IOR/EOR methods.
The field has been redeveloped using an average 20 to 25 acres per WF pattern, which were either inverted 5-spot or 7-spot configurations. Since 2005, selective completion strings were installed with side pocket mandrels controlling the vertical distribution and volume of water injected with each mandrel covering a group of sand layers. The selective completion was successfully implemented, and provided a significant boost in oil production. The field has multiple opportunities to increase oil production. Before performing these solutions, it was important to understand the producer-injector connectivity across the entire field. The capacitance resistance model (CRM) provided some insights on the interwell connectivity within the reservoir between injectors and producers, however it is difficult to correlate the CRM simulation results with multiple geological interpretations and reservoir characterizations using geostatistics.
This paper presents a WF surveillance program with a focus on incremental oil recoveries from multiple stacked-sands. The injection and production profiles were very useful in improving WF conformance. The water injection profile was measured with a rigless tracer string. The production profile was initially estimated using petrophysical properties and fractional flow curves due to the difficulty of running a production logging tool (PLT) from every pumping well in complex multilayer reservoirs. It was important to validate the production profile with a couple of PLT pilot wells. In these pilot wells, we employed a Y-tool for a connection with the electric submersible pump (ESP) on one side; and the other side of the Y-tool served as a pass through for the PLT.
The production and injection profiles provided insights for injector-producer well correlations between productive sand layers. Based on the well data, reservoir simulation models were created and have added value to our reservoir surveillance program, significantly increasing oil production, WF sweep efficiencies, and incremental oil recoveries in the mature LCI WF oil field.
Multi Attribute Approach for Quantifying Competing Time Lapse Effects and Implications for Similarity Indicators in Data Assimilation
Authors: Santos Juliana Maia , Schiozer Denis José , Davolio Alessandra 
Published: October 2020

Abstract
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.