bluuebunny/crossref_metadata_2025 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

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

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

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

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

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

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

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

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

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.
3-Axis Borehole Gravity: Method and Application to CO2 Storage Monitoring and Oil/Gas Production
Authors: Topham Adrian , Lofts Jeremy , Du Zhijun , Espie Tony , Zett Adrian , Krahenbuhl Richard , Li Yaoguo
Published: October 2020

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 CO2capture 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 CO2 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 CO2 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.
Synergy Between Commodity Molecules and Nanoparticles as Steam Mobility Control Additives for Thermal Oil Recovery
Authors: Fehr Adam , Telmadarreie Ali , Berton Paula , Bryant Steven
Published: October 2020

Achieving mobility control of steam in porous media has been approached by the implementation of foaming surfactant additives; however, foam generated with surfactants lack stability at high temperatures (≥ 200 °C). In this study, a nanofluid was formulated for steam co-injection to address common issues with surfactants as steam additives. The nanofluids were formulated using synergistic interaction of nanoparticles and surfactants (both readily available) to address thermal stability, and foam stability at the conditions encountered in thermal enhanced oil recovery processes such as steam assisted gravity drainage.

Zeta potential analysis, static tests, and high temperature aging tests were performed to obtain the ideal mixture of nanoparticles and surfactants. A steam/foam core flooding apparatus was used to evaluate the mobility reduction of nanofluids using multiple differential pressure transmitters situated along the length of a 40 cmsand pack to confirm foam propagation. The tests were first conducted at 200 °C with nitrogen as a non-condensable gas carrier. Upon confirming nanofluid mobility reduction characteristics at 200 °C with gas, a successful nanofluid was co-injected with superheated steam at backpressure corresponding to a saturation temperature of 200 °C inside an oven with that set point. Mobility reduction characteristics were obtained for each formulation by normalizing the differential pressure of additive multiphase flow to their respective baselines.

Statictests and high-temperature core floods demonstrated a strong synergy between appropriate combinations of surfactants and nanoparticles.None of thesurfactants and nanoparticlesyielded mobility reduction when used on their own; however, when selected according to a design basis, nanoparticle/surfactant mixtures exhibit strong mobility control with steam as well as hot gas. The foam produced in the porous media was held in a visual cellat 150 °C for 24 h with no loss in foam height. Furthermore, foam generated with the nanoparticle-surfactant hybrid remained stable in static tests in the presence of heavy crude oil for more than one week.

The primary novelty of this study is the ability to use less exotic surfactants and nanoparticles as steam mobility control agents, increased foam stability in the presence of oil, and demonstrable synergy between commodity molecules (i.e., surfactant) and nanoparticles at steam flooding conditions. The successful additive mixture was developed with scalability in mind such that manufacturing will not hinder the feasibility of scale-up for industrial use.
Reducing the Volume of Water Needed for Hydraulic Fracturing by Employing Natural Gas Foamed Stimulation Fluid
Authors: Malpani Raj , Daeffler Chris , Verma Sandeep , Beck Griffin , Ogunyemi Taofeek
Published: October 2020

As part of a Department of Energy (DOE)-sponsored program, an optimal thermodynamic pathway to transform natural gas (NG) into pressurized NG suitable for use as the internal phase in a foamed fracturing fluid has been already developed. Using NG foamed fracturing fluids reduces the enormous water requirements for stimulation by as much as 60% to 80% and poses benefits for productivity in water-sensitive formations. This study aims to extend the investigation to characterize the hydraulic fracture geometry and quantify the expected production when using an NG foam fracturing fluid. Using validated models, we provide a comparative analysis to determine the advantages of using natural gas foams relative to conventionally used slickwater, linear gel, and crosslinked fluid.

A full 3D reservoir model in a Duvernay Shale formation was constructed. Fundamental laboratory and pilot field tests data was collected for the NG foam fluid properties for numerical modeling. Rheology, friction, and leakoff properties of the fracturing fluids were incorporated in creating a numerical model. A 3D-complex hydraulic fracture simulation model incorporating 1D and 2D particle transport models were used. A numerical reservoir simulation for different sensitivity scenarios was incorporated for fracture modeling and gas production evaluation. Owing to lower density than conventional liquid column, the NG-foamed fluids are likely to result in higher surface pressure.

A reduced pump rate with NG foamed fracturing fluid leads to a lower frictional pressure loss in tubing, without compromising the ability to place the desired amount of proppant in the formation. The non-Newtonian shear-thinning NG-foamed fracturing fluid exhibits a higher, effective viscosity that enables effective transport of the proppant. Modeling results indicate that the overall fracture geometry and proppant placement is much better for NG-foamed fluids than high-volume slickwater needed to pump the same amount of proppant for well spacing and a field development plan. The simulated production performance for medium-viscosity fluids such as NG-foamed fluid, linear gel, and high-viscosity slickwater, is better than that of low-viscosity slickwater or high-viscosity crosslinked gel fluids. A low-viscosity fluid results in proppant settling and dunning, resulting in lower conductive height of fractures while the high viscosity treatment uses less fluid, so the surface area created is less and there is potential for height grow out of the target formation.
Complex Fracture Hits Modeling and Analysis for Parent-Child Wells in Eagle Ford
Authors: Yu Wei , Fiallos Torres Mauricio Xavier , Liu Chuxi , Miao Jijun , Sepehrnoori Kamy
Published: October 2020

Shale field developmentfinds significant challenges when operators have to define optimal spacing of infill wells and further fracture optimization, based on biased understanding of the physical phenomena behind fluid flow in complex unconventional reservoir systems. Although proper modeling has been employed in other studiesto address the detrimental impact of well interference, this study poses how these fracture hits can be beneficial after estimating their impacts in hydrocarbon cumulative recoveries. This study includes spatial variations in fracture conductivity and complexity on fracture geometries of inter-well interference. Furthermore, a non-intrusive embedded discrete fracture model (EDFM) method has been employed to generate these complex scenarios and investigate the impact of well interference multi-well field models. Based on a robust understanding of fracture properties, real production data and wellbore image logging, multiple comparison are performed to address the effects of accounting for inter-well fracture hits on field pressure and production response. First, according to updated production data from Eagle Ford, a model was constructed to perform two (parent) wells history matching. Later, three child wells were included so thatoptimal cluster spacing was recommended considering interwell interference and the distance to thoselong-induced fracture hits. Finally, a field case is presented where the effects of long interwell fractures are evaluated in a nine-well numerical model and contrasted to a scenario without fracture hits. This case is an extension of the work presented by Fiallos et al. (2019) where fracture diagnostic results from well image logging were employed to perform sensitivity analysis on attributes of long interwell connecting fractures.

The simulation results show that long induced fracture hits can be addressed by correlating inter-well wellbore image logs, which will support the occurrence of well interference. Because of these interwell long fracture hits, favorable communication is originated and, thereby, it enhances the oil recovery of the child wells by expanding their drainage influence towards further zones of the reservoir. Likewise, the higher permeabilities in this fracture hits reduce the bottomhole pressure drawdown. As a consequence, the model became a valuable stencil to decide the cluster spacing, and to optimize the hydraulic fracture treatment design. The simulation results were applied to the field successfully to afford significant reductions in offset frac interference by up to 50%.
Nanoparticle-Stabilized Strong Foam for EOR in Fractured Reservoirs
Authors: Xuezhen Wang , Mohanty Kishore
Published: October 2020

Many carbonate reservoirs are fractured. Injection of EOR agents (miscible gases or surfactant solutions) into fractured reservoirs leads to flow through the fractures and bypassing of the matrix. These fluids can be directed into the matrix if the fluids are injected into the fractures as a foam. The goal of this work is to develop a strong foam for injection into fractured reservoirs by using nanoparticles. Ethyl cellulose nanoparticles (ECNP) were prepared from ethyl cellulose with 48% ethoxy groups. ECNP were used to stabilize foam with and without a nonionic surfactant, CH3-70PO-100EOH. The size of the ECNP prepared was smaller than 200 nm with a narrow size distribution. The addition of ECNP to a nonionic methoxy surfactant (CH3O-70PO-100EOH) helped with foamability and foam stability compared to surfactant only samples. The foam stability in the presence of oil was also improved by ECNP. Foam flooding with an oil-wet fractured Texas Cream limestone indicated that the oil recovery was much higher with ECNP/nonionic surfactant compared to a conventional AOS foam and also the immiscible gas (methane/ethane gas mixture at a molar ratio of 1:1) alone. The oil recovery was 71% with the injection of 80% quality foam using 500 ppm ECNP and 1.5 wt% nonionic surfactant at 1wt% NaCl concentration. This work illustrates that the oil recovery can be improved with a strong foam even with gases that are not multi-contact miscible.
Automated Procedure for Quantifying ISIP and Friction Losses from Stage by Stage Hydraulic Fracture Treatment Falloff Data
Authors: Alwarda Fahad , Liu Guoqing , Nikolaou Michael , Ehlig-Economides Christine
Published: October 2020

While diagnostic fracture injection tests (DFIT) data is relatively rare, most hydraulic fracture treatment stages in multiple transverse fracture horizontal wells (MTFHWs) follow pressure and rate data during pumping with several minutes of pressure falloff data after the end of pumping. Recent papers have shown value in applying analysis developed for DFIT data to hydraulic fracture treatment falloff (HFTF) data. As is often the case with routinely acquired operational data, the time required for analyzing HFTF datasets for each of the treatment stages in a long horizontal well may be prohibitive. This paper offers an automated analysis procedure that starts from standard treatment pressure and rate data and produces estimates for wellbore and perforation friction loss, near wellbore tortuosity friction loss, and the instantaneous shut in pressure (ISIP).

Steps in the automated procedure include isolating the HFTF data from the rest of the hydraulic fracture treatment data (which is typically subject to hydraulic hammer), applying an optimized low-pass filter (LPF), and computing the friction and ISIP estimates by automating a previously published graphic procedure. We employ the automated analysis on field data that was previously analyzed by hand. Then we compare and contrast between the two analyses.

The comparison between manual and automatically analyzed fracture treatment falloffs demonstrates that the automated procedure reproduces the previous analyses except in a few cases that pose special challenges to any analysis. The new field dataset results demonstrate the approach is practical for field application.

Variations in friction loss and ISIP estimates along MTFHWs provide data useful for well and pad completion design decisions related to perforating strategies, and fracture and well spacing.
Mini Fracs Can Provide Reliable Formation Permeability in Unconventional Shale Reservoirs
Authors: Uzun Ozan , Uzun Ilkay , T. AlHashmi Omar , Kazemi Hossein
Published: October 2020

Mini frac, or diagnostic fracture injection test (DFIT), is a short hydraulic fracturing test that provides formation break-down pressure, minimum horizontal stress, and reliable value for formation permeability of shale reservoirs. The calculated formation permeability is particularly more reliable from the analysis of the second cycle of the mini frac test because the fracture is already created. In this paper, we present a simple technique to analyze and interpret DFIT data similar to the analysis of the classic drillstem test (DST) data in vertical wells. The only difference is that in DFIT pressure-time characteristics approximate the linear flow regime while in DST, pressure-time behavior follows the radial flow regime.

In general, DFIT analysis provides matrix permeability while the analysis of long-term pressure decline of the production data yields stimulated formation permeability, which can be attributed to microfracture permeability. Thus, we will show how we utilize the permeability from DFIT, and the permeability calculated from the production decline data of production wells (i.e., multistage hydraulically fractured wells) to construct a viable dual-porosity model to assess the performance of wells under primary production and gas injection EOR. We also compare our results with those of Nolte G-function.

The paper includes numerical modeling of two-phase nonlinear flow, analytical solution methodology from multi-phase flow, experimental data, and field data to illustrate the viability of our interpretation method. Furthermore, our analysis technique is simple because it only uses pressure falloff data points during the shut-in period of the DFIT. Not only the method is confirmed by numerical modeling, but it is also verified by pressure falloff from laboratory data.
New-Type Water Controlling Technology for Improving Production Performance of Offshore Heavy-Oil Reservoir with Active Bottom Water
Authors: Shang Baobing , Liu Yigang , Meng Xianghai , Bai Jianhua , Wu Huaxiao , Yu Fahao , Mu Mei , Li Yue
Published: October 2020

This paper introduces a new water controlling technology to manage water production and improve production performance in an offshore oilfield in Bohai, China. Q oilfield is a typical heavy oil reservoir, mainly developed by horizontal wells. One of the main challenges for the production is the high water cut. Most of the wells experience water breakthrough within one year after put into production and produce for long time with water cut higher than 90%. Besides, the strong anisotropism of the formation aggravates the water breakthrough and makes water control work more difficult.

In 2019, a new combined water control technology was applied to manage water influx in horizontal completions. In this technology, the annulus between the wellbore and the inner ICD (inflow control device) /AICD (Autonomous inflow control device) screen is filled with light-but-hard particles. In this paper, the barrier built by the fine particles is called continuous-packer. The existence of this barrier plays a similar role to mechanical packers between each ICD/AICD screen, thus the axial flow of the produced liquid is prevented in the horizontal section. Besides, the ICD/AICD screen is equipped to limit the liquid inflow of each segment based on the design. The purpose of equalizing production profile of the horizontal section achieves through the cooperation of the continuous-packer and the ICD/AICD screen.

Untill now, this new technology has been used in more than 6 wells in Q oilfield, including both producing horizontal wells with high water cut and newly drilled wells. The production results show that the water cut reduces about 10% and the oil production increases for the high water cut well. The water breakthrough time and the water cut increasing rate is slower for the new wells comparing with near wells.

The successful application of this technology demonstrates its validity for the offshore heavy-oil reservoir with active bottom water. It also provides a new method for the water controlling work for the offshore wells in Bohai oilfield. A detailed plan has been finished to implement this technology in more wells in 2020.
Enhanced Wellbore Leak Localization with the Estimation and Removal of Guided Wave Noise Using Array Hydrophone Logging Data
Authors: Ge Yao , Wang Ruijia , Ang Yi Yang , Tracadas Philip , Wu Xiang , Padhi Amit
Published: October 2020

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.
A Self-Learning Process Modeling Method to Optimize Upstream Operations
Authors: Guddeti Ravikishan , Sankaran Sathish
Published: October 2020

Upstream production optimization is concerned with optimizing the entire hydrocarbon value chain from reservoir to sales. In this quest, managing the process facilities to maximize productivity is a critical part. Traditionally, operating companies have addressed it by running offline scenarios to develop a playbook or build physics-based dynamic simulation models. However, a typical offshore facility undergoes significant changes to its operations during its life cycle, which makes the model management tedious and expensive. Few attempts have been made to build data-driven digital twins (machine learning) but they often lack the ability to provide explainable models and lack physical insights. In this work, we propose to use a fast, hybrid, self-learning dynamic process modeling method from routine plant measurements that can be used for real-time forecasts, scenario modeling, process optimization and control.

A reduced order modeling method based on input-output dynamic mode decomposition (ioDMD) has been adapted to develop a dynamic process model based on historical data collected from plant sensors. First, we benchmarked the proposed approach with a dynamic simulation model (commercial simulator) using a designed input sequence for training. The ioDMD model simplifies the physical mechanisms to a low dimensional form. Next, we applied the method to an actual offshore deepwater facility based on plant measurements. In both cases, the ioDMD method provided very good predictions without any human intervention. Unlike black-box data-driven methods, the ioDMD method uses an interpretable approach that can be used to explain causal relationships. Observability and controllability of the proposed model can also be easily understood.

The proposed ioDMD method provides a unique and sustainable way to combine advanced analytics and physics to develop an explainable dynamic model for the process facility that can be effectively used to assist operations in optimizing performance. The lightweight model lends itself naturally to fast computation that are required for optimization and process control (including IoT edge devices).