bluuebunny/crossref_metadata_2025 huggingface dataset:
CFD Modeling of Pseudo-Transient Perforation Erosion in a Limited-Entry Completion Cluster
Authors: Kebert Brent A. , Miskimins Jennifer L. , Soehner Gage , Hunter William
Published: January 2023

Hydraulic fracturing in limited entry (LE) completion designs relies on maintaining a high bottom hole treating pressure (BHTP). LE requires high perforation friction in order to maintain an even distribution of the hydraulic fracturing slurry. As sand exits the perforations, the perforations start to erode. The erosional change in the perforation alters the desired perforation friction and subsequent BHTP. As operators rely on multistage hydraulic fracturing to generate economic production, the issue of perforation erosion becomes inherently repetitive from stage to stage, and cumulatively a significant issue. The industry has seen how a perforation can change from a before-and-after perspective with downhole cameras and imaging techniques before and after treatments. However, a more detailed understanding of the dynamic process of perforation erosion can give a better expectation of perforation performance throughout a hydraulic fracturing treatment and not just pre-treatment compared to post-treatment.

Computational fluid dynamics (CFD) is a quickly emerging tool in the industry. CFD aims to model fluid flow by numerically solving the Naiver-Stokes equations within a specified domain. Along with modeling fluid systems, CFD has the capability to model dispersed particles within the fluid. Once the particles are introduced into the fluid, the domain can also be eroded away within the CFD model. By utilizing the erosional capabilities of CFD, paired with the flow of a hydraulic fracturing slurry, perforation erosion can be investigated transiently throughout an entire hydraulic fracturing stage.

This work presents a better dynamic understanding of perforation erosion rather than just a "before versus after" comparison. The CFD modeling methodology used to achieve the correct erosional pattern observed in the field is presented. Throughout this work, six different hydraulic fracturing completion parameters are investigated to determine the respective roles in perforation erosion. The six parameters include proppant size, proppant concentration, proppant sphericity, fracturing fluid viscosity, initial perforation diameter, and proppant concentration ramping schedules. By investigating the impact that controlled design parameters have on perforation erosion, perforation erosion can be better anticipated to deliver improved completion results.
Parent Well Characterization by Comparative Analysis of Initial and Second Flowback
Authors: Cao Chong , Moussa Tamer , Dehghanpour Hassan
Published: January 2023

Infill drilling is becoming a common practice for more efficient development of tight reservoirs. However, child-well stimulation may lead to a parent-child well interference or a fracture hit. To mitigate the negative impacts of a fracture hit, the parent well is preloaded before the stimulation of child wells. Then, the injected water during the pre-loading period is later produced back to the surface. The preloading flowback (second flowback) data of parent wells may provide an opportunity for fracture charactrization. The main objective of this research is to compare the responses of initial and second flowback to capture the changes in fracture characterstics after production and preload processes.

We construct rate-normalized pressure (RNP) diagnostic plots on both initial and second flowback (IFB and SFB, respectively) of six multi-fractured horizontal wells completed in Niobrara and Codell formations in DJ Basin. In general, the slope of RNP versus MBT during the SFB period is higher than that during the IFB period, except for well 1. We estimate the changes in average effective fracture volume (Vef) by analyzing the changes in the RNP slope and total compressibility during these two flowback periods. Compared to the IFB period, the Vef is generally decreased during the SFB period.

The loss percentage of effective fracture volume (RVef) is estimated at 10-40%. We also compare the drive mechanisms for the two flowback periods by calculating the compaction drive index (CDI), hydrocarbon-drive index (HDI), and water-drive index (WDI). The dominant driving mechanism during both flowback periods is CDI, but its contribution is reduced by 12% in the SFB period. This drop is generally compensated by a relatively higher HDI during this period. Finally, we investigate the effects of duration of production (tp) on RVef. There is a positive correlation between tp and RVef during the two flowback periods. Therefore, the loss of effective fracture volume might be attributed to the pressure depletion in fractures caused by the long production period (more than 800 days).
The Economics of Refracturing in the Haynesville
Authors: Barba Robert , Villarreal Mark
Published: January 2023

The Haynesville organic shale has just under 3600 wells, completed before mid-2016, with cluster spacings over 50 feet. This wide spacing resulted in many wells having missed or bypassed reserve recovery due to inefficient completions. Understandably these wells would be prime candidates for refracturing. Refracs have the advantage of lower up-front capital cost, fewer operational issues that rely on functioning supply chains, and significantly lower carbon footprint than new wells that need to be drilled prior to completion. However, a frequent concern is that refracs in the Haynesville cannot compete with new well economics. We looked at both public and operator refrac data to identify the criteria that makes a good Haynesville refrac candidate. We offer case studies to show the economic performance of 45 Haynesville liner refracs using actual post refrac decline data and current well costs. Both the economic value of the post refrac production is included along with the economic value of protecting infill wells from asymmetric fractures. An analysis of the completion design for a typical Haynesville refrac is also provided to demonstrate that significant upside remains for improvement if "best practices" are applied. These include Extreme Limited Entry (XLE), zero-degree top perforations per cluster, larger diameter expandable liners, and microproppant in the prepad.

In our data set, the typical Haynesville refrac completion has very low pressure drops (520 psi pre-erosion) across the perforations. With average treating pressures of 9500 psi there is limited room for higher pressure drops due to a 10,500-psi surface pressure limit. And so, the available delta pressure for treatment is limited to +/- 500 psi when a 2500 to 3000 psi pressure drop is recommended to maximize cluster efficiency (Hausveit 2020). With the author’s recommended practices, over 2500 psi of excess friction losses can be avoided, providing adequate margin to achieve the recommended pressure drop. With increased cluster efficiency, increased recovery factors should follow. While Haynesville refracs have successfully increased estimated ultimate recovery (EUR), when "best practices" are followed, the economic returns should be highly competitive with new-well completion economics. If the refracs had the 60% average cluster efficiency values from several studies done on poorly diverted treatments (Weddle 2018 and Miller 2011) the total NPV10 for a P50 refrac is equivalent to the new well NPV10 values.
Parent Well Strategies in the Bakken, Trends and Outcomes
Authors: Schmidt Darren , Min Kyoung Suk , Guillot Stephen
Published: January 2023

Significant attention has been placed on well-to-well interactions since the beginning of infill development in unconventional plays. Asymmetry of hydraulic fractures toward pressure-depleted parent wells has been attributed to poor performance of early in-fill completions (Xuyang et al., 2019). Factors include well spacing, timing, and specifics of colocation. Various operators have developed strategies to mitigate these effects and improve production for new spacing units. This paper reviews the historical response to parent–child well development in the Bakken, characterizes trends, and examines the techniques that have been applied and their efficacy. Results provide evidence that development decisions continue to be largely driven by surface constraints, suggesting there is more work needed to improve future child well performance. There is a trend away from refracturing for most operators, particularly as part of drilling spacing unit (DSU) redevelopments involving drilling of multiple child wells. Common mitigation techniques include fracturing the child well closest to the parent well first and increasing well spacing.
Observations, Learnings, and Validation of Conductive Fracture Imaging
Authors: Reshetnikov Anton , Nazarova Anna , Taylor Scott , Haffener Jackson , Langton David , Biholar Alex , Anderson Sloan
Published: January 2023

A novel diagnostic processing technique called Conductive Fracture Imaging (CFI) measures hydraulic and conductive fractures using microseismic events as a source. The method was applied to three datasets located in onshore unconventional formations in the United States. CFI results were in all cases first delivered independent of any external diagnostic data and only subsequently compared to multiple diagnostics such as microseismic, fiber cross-well strain (CWS), 3D seismic, and recovered core under supervision of Devon Energy’s Subsurface Team.

The comparison reveals a reasonable agreement of the CFI results with cross-well strain for both height and transverse conductive fracture growth. CFI was able to image fractures out 1 mile from the observation lateral, with fractures imaged in areas of no microseismic activity. Furthermore, CFI successfully quantified the height growth of fractures aligned with the pre-existing faults and how natural structures influence conductivity fracture distribution.

CFI reveals a valid relationship with cored & interpreted conductive, hydraulic, and natural fractures. The method provides dynamic images showing fracture morphology from the near-wellbore into the far-field reservoir. Complimentary analytics of relationships between CFI and reservoir properties, limited entry perforation designs, stress shadowing, and depletion effects may generate significant new observations and key learnings to industry as this technique is more broadly adopted.
The Effect of Casing Deformation on Frac Plug Sealing Element Performance
Authors: Yoshida Hiroaki , Jafri Syed , Okura Masayuki , Takahashi Shinya , Li Wei
Published: January 2023

Sealing element of frac plugs have crucial roles to isolate target zones of the well in hydraulic fracturing. If the zonal isolation by the sealing element is not adequate, it can result in erosion of the casing. The effect of casing deformation on sealing performance of sealing element is not well researched or understood. To study the effect of casing deformation on sealing performance, finite element analysis of sealing element in deformed casing was conducted in this study to assess the effect of casing deformation on sealing performance. In this study, finite element simulation of a full frac plug with three different casings ovalities (0%, 2%, and 5%) and three different sealing element designs (O-ring type, short type, traditional long type) was conducted to evaluate deformation behavior and sealing performance of the sealing elements in the deformed casings. Compression pressure on the casing by sealing element after the plug is set in the casing and the risk of leak were discussed and compared for each design. In the casing with 0% ovality, all the sealing element designs established contact with inner surface of the casing when setting force is applied. However, for the O-ring type design, area in contact with the casing was small and it may result in leak and erosion in the actual well if there is a small dent or deformation on the casing. When there is deformation and ovality in the casing, the minor ID has a smaller ID and a major ID has a larger ID compared to nominal ID of the casing. In the casing with 2% and 5% ovality, neither O-ring type nor short type sealing element could contact the major ID of the casing and there was a gap between inner surface of the casing and the sealing element. This gap can cause erosion of the frac plugs and casing when fluid passes through the gap. In contrast, traditional long type sealing element contacted both major and minor IDs of the casing and no gap was observed. This result indicates that there is a potential risk of insufficient isolation of target zones and erosion of casings in actual well condition if frac plugs with small sealing element is used. Since there are various types of frac plugs with different sealing element designs, this study helps to select proper frac plugs with good sealing element design and mitigate the risk of erosion of casings and plugs.
Probabilistic Quantification of Size Segregation and Screen-Out of Microparticles Subject to Electrostatic Forces
Authors: Leonardi Christopher R. , Di Vaira Nathan J. , Laniewski-Wollk Lukasz , Aminossadati Saiied M. , Johnson Raymond L.
Published: January 2023

A growing number of hydraulic fracturing stimulation treatments rely solely on the deployment of 100-mesh (i.e. 150μm) proppant. Further, these may be preceded by post-pad injection of microparticles (i.e. 5-75μm) with the intent of activating natural fractures. The objective of this work is to describe fundamental new insights on the behaviour of polydisperse microparticle suspensions in hydraulic fractures in terms of screen-out and leak-off.

This study was undertaken using a high-fidelity computational model of suspension transport based on the lattice Boltzmann method (for fluid mechanics) and discrete element method (for particle mechanics). The approach has been previously validated against analytical solutions and experimental data for suspension flows in channels, and applied to study fundamental aspects of size segregation and clogging (i.e. screen-out) of microproppant. This fully-resolved modelling approach captures two-way hydrodynamic coupling, electrostatic interactions, fracture roughness and tortuosity, and non-Newtonian fluid rheology. The sole assumption of significance is that particles are assumed to be perfectly spherical.

One of the challenges of deploying micron-scale proppant in hydraulic fracturing stimulation treatments is poor control of the particle size distribution. This inevitably results in a proportion of the injected proppant being small enough to be susceptible to electrostatic (rather than just mechanical and hydrodynamic) forces. This study clearly demonstrates the increased probability of screen-out that results as a consequence of electrostatic particle-particle and particle-wall interactions, and shows how this effect reduces as the minimum particle size increases. Analysis of particle leak-off off into transverse cleats also demonstrated how the combination of cleat width and particle size result in the formation of occlusions at cleat intersections, the reduction of leak-off rates in cleats, and the retardation of proppant transport in the primary fracture. These findings are significant because tight size control is not economically feasible for microproppant, and so hydraulic fracture engineering must accommodate their characteristic behaviours. This will result in more effective stimulation of coalbeds, but is applicable to all jobs where small proppant is proposed for injection into natural fracture systems.
Integrating Pulsar Technology with Reservoir Centric Fracturing Approach to Restore the Production in a Mature Tight Oil Field
Authors: Slimani Karim , Khetib Tayeb , Monfared Hashem , Lamali Rabah , Bendali Amor Nasrine , Skender Mohamed Semch-Eddine , Mezali Farid , Chekou Hakim , Belaifa Lotfi
Published: January 2023

Tight oil reservoirs are of paramount importance for an operator holding several fields with important oil potential in Illizi basin, southeastern Algeria. Formation tightness, the presence of nonconnected sand lenses, and the lack of a geological model made it very difficult to maintain the oil production in the studied Devonian reservoir. Consequently, the service company and operator adopted an integrated approach to devise solutions that could restore production in a mature tight oil field that had been closed in 2011.

Construction of a geological model for the studied reservoir was challenging because of the high uncertainty in water saturation interpretation caused by the formation water properties (fresh water). A multifunction pulsed neutron service was proposed to provide standalone cased-hole formation evaluation and reservoir saturation monitoring. A unique modeling approaches was applied to characterize the studied tight reservoir and evaluate the reserves based on advanced uncertainty analysis. A hydraulic fracturing design workflow in a reservoir centric stimulation to production software was developed using an integrated approach (geological and geomechanical models) to place the fracture in the optimum reservoir quality and connect the sand lens bodies.

Two existing wells were selected to run with the pulsed neutron service, resulting in acquisition of comprehensive reservoir rock and fluid content data. The interpreted logs served to reduce the uncertainty in water saturation modeling and to enable perfect history matching of the producing wells. The constructed geological model was the basis for improving the stimulation designs and maximizing production for future wells. With significant oil initially in place (STOIIP) estimated from the model, the field showed more promise than the previous recorded recovery factor of less than 1%. The field development plan (FDP) identified the location of 20 new infill drilling wells targeting the sweet spots and considering the optimal well spacing. In addition, the plan specified a systematic hydraulic fracturing stimulation job for each newly drilled well to connect and produce the sand body lenses. Recently, a successful campaign of hydraulic fracturing operations was executed on four wells, allowing the operator to resume production from the field. The fracturing performance minimized water cut despite the water-oil-contact (WOC) proximity, and it enhanced the oil recovery.

The developed integrated approach has already shown its effectiveness in returning a field to production and improving oil recovery. The approach can be replicated on subsequent wells in the field as well as on similar tight reservoirs all over the world.
Single Well Chemical Tracer Application for Chemical EOR Pilot Project: Principles and Best Practices
Authors: Khanifar Ahmad - , Abdul Manap Arif Azhan
Published: January 2023

Chemical injection has been emerged to be one of the processes that can improve oil recovery from major Malaysian offshore oilfield which is currently under waterflooding. Alkali-surfactant (AS) process was identified to be an optimized chemical system for this application. The chemical recipe and formulation for the field trial tests were obtained from comprehensive laboratory experiments studies. Pilot tests were designed and executed to evaluate the effectiveness of this chemical injection prior full field-scale implementation.

Single well chemical tracer (SWCT) technique has been utilized to determine the residual oil saturation (Sor) before and after chemical injection in a one-spot pilot and cost-effective manner approach. Two wells and two different chemical formulations were investigated for AS injection responses and four tests were conducted in sequence. The objectives were to validate the laboratory results, assess the critical chemical process parameters such as Sor reduction, adsorption, injectivity, and obtain an operating experience at a harsh offshore environment with high reservoir temperature. Sea water treatment and softening process was needed to protect chemical slug from high-salinity and high-hardness environment.

Favorable results achieve where successfully mobilized substantial amounts of Sor, chemicals easily mixed with no injection problems encountered, and no measurable dilution effects that indicated fluids travelling outside of test zones. The initial Sor waterflooding observe to be 0.16 and 0.27 for well 1 and well 2 respectively, while, interestingly, both wells show a 0.04 increase in Sor after first pilots were completed. It is most likely due to a shift in rock wettability toward more water wet. Competing reactions of alkaline was a major concern. Pre-flush and post-flush buffer of soft water was designed to minimize these reactions and allow surfactant to work in more favorable lower salinity water. Fortunately, Sor results show that the competing reactions were not severe enough to prevent the AS systems from working. The lowest Sor were 0.06 and 0.08 in the case of 1.5 PV and 1.0 PV soft-water buffer, respectively. It shows that Sor was not significantly reduced with additional 50% PV buffer. Even in the case of 0.15 PV buffer and 23,000 ppm salinity, Sor decreases only by 25%. It demonstrates that some degree of success will be gained even in the worst case where the salinity was reduced by 60%.

This paper presents the principals of SWCT pilot application for a chemical EOR (CEOR) project case study and share the best practices and lessons learnt were achieved from these field trail tests. This paper can be used as a technical reference and guideline for upcoming CEOR projects and promote a detailed development plan which can potentially address various challenges that are often encountered in implementing chemical flooding, particularly at offshore oilfields.
Permeability Prediction of Carbonate Cores With Gaussian Process Regression Model
Authors: Bu Xingang , Saleh Hassan , Han Ming , AlSofi Abdulkareem
Published: January 2023

Machine leaning (ML) methods are widely adopted in predictions affected by various factors. This paper presents a step-by-step workflow of applying a ML approach to develop a heterogeneous permeability prediction model from the CT images of core samples. In this work, over ten thousand 3-D sub-image were randomly extracted from the CT images of two heterogeneous carbonate core samples. The permeability of each sub-image is simulated using pore network modeling (PNM) method. Ten features including porosity, pore size, surface area, specific surface area and connection coefficient etc. are extracted from sub-image by a statistical method. Three training datasets were built with features and permeability. Each set of training data is input into a ML model pool, which contains 19 regression models of 5 types including linear regression models, regression trees, support vector machines, Gaussian process regression models and ensembles of trees. Then, regression models are trained to identify the one that can yield the best permeability prediction. The trained model with the highest R-Squared value is selected for permeability prediction from binary CT images. Overall, comparing the training outputs indicate that Gaussian Process Regression models (GPR) correlate features and permeability well. For the tested heterogeneous core plugs, the exponential Gaussian Process model performs the best. The R-Squared values of the three sets of training data are 0.88, 0.87 and 0.91 respectively. Afterwards, the selected ML model was tested with additional data, and the R-squared value of each test dataset was greater than 0.85, confirming a strong predictive performance. The trained model based on ML method eliminates the conventional time-consuming operations including distance transformation and watershed segmentation. It also avoids excessive memory consumption, which makes the method suitable for images with large size. The paper provides a way to develop an alternative approach of PNM simulation method for permeability prediction from CT images.
A Comparative Study for Deep-Learning-Based Methods for Automated Reservoir Simulation
Authors: Maarouf Alaa , Tahir Sofiane , Su Shi , Kada Kloucha Chakib , Mustapha Hussein
Published: January 2023

Reservoir simulation is essential for various reservoir engineering processes such as history matching and field development plan optimization but is typically an intensive and time-consuming process. The aim of this study is to compare various deep-learning algorithms for constructing a machine-learning (ML) proxy model, which reproduces the behavior of a reservoir simulator and results in significant speedup compared to running the numerical simulator.

Initially, we generate an ensemble of realizations via the reservoir simulator to train the different ML algorithms. The data set consists of a comprehensive set of uncertainty parameters and the corresponding simulation data across all wells. The system utilizes recent advances in deep learning based on deep neural networks, convolutional neural networks, and autoencoders to create machine-learning-based proxy models that predict production and injection profiles as well as the bottomhole pressure of all wells. Thus, the proposed workflows replace the time-consuming simulation process with fast and efficient proxy models.

In this work we provide a comparative study of various ML-based algorithms utilizing deep neural networks and convolutional neural networks for constructing a surrogate reservoir model. The trained models can simulate the behavior of the physics-based reservoir simulator by correlating uncertainty parameters to various history-matched reservoir properties. The algorithms were tested on a mature oilfield with a notable number of wells and several decades of production and injection data. We analyze the performance of each ML approach and provide recommendations on the optimal one.

The best performing workflow for building the ML proxy model consists of two steps. The first step uses stacked autoencoders to learn a low-dimensional latent space representation of the highly dimensional simulation data. This step allows to reduce the complexity of predicting the simulation data and enhances the prediction quality. The following step constructs an ML model to predict the latent space features from input uncertainty parameters and produces highly accurate results.

Reservoir simulation is of paramount importance for various reservoir engineering workflows. Traditional approaches require running physics-based simulators for multiple iterations, which results in time-consuming and labor-intensive processes. We implement and compare several deep-learning-based methods to construct ML proxy models that automate and remarkably reduce the runtime of the reservoir simulation process.
Electromagnetic Technologies for Reservoir Mapping: Technology Review
Authors: Komies Saleh , Alshaya Abdulaziz
Published: January 2023

Monitoring fluid saturation and reservoir surveillance are essential techniques for effective reservoir management. As reservoirs age and water sweep advances, it becomes more difficult to find infill well placements. Reservoir scale saturation surveillance technologies offer a novel and promising way to optimise hydrocarbon recovery through development decisions. In addition, it can help evaluate sweep efficiency and improve reservoir simulation models. There are currently various proven electromagnetism approaches for reservoir scale saturation monitoring, including cross-well-EM (X-well-EM), Surface to Borehole EM (STB-EM), Borehole to Surface EM (BTS-EM), and EM through the casing. This study will examine all EM technologies.

Each technology has unique benefits and drawbacks that determine its field applications and objectives. The X-well-EM technology offers a cross-sectional resistivity profile at the inter-well level, which may be used to map oil zones that have been bypassed. Similar to the surface to borehole and borehole to surface technologies, the surface to borehole and borehole to surface technologies detect resistivity changes between transmitters and receivers for the same purpose. The benefit of these technologies is that they may be utilised for both one-time and recurring surveys. This feature permits the implementation of a feasible flood monitoring system throughout the field's lifetime.

This paper will thoroughly discuss the design for each technology and the survey planning. In addition, the challenges and critical criteria for conducting a successful survey will be discussed, including measurement accuracy, inter-well spacing, depth, signal-to-noise ratio, operating cost and time, completion kinds, and the optimal frequency range. Finally, we will present the end result that makes electromagnetism an intriguing concept emerging in the field of reservoir surveillance. Examining and contrasting the various EM survey approaches in order to highlight the best practice and its potential for field growth.
Numerical Analysis of Condensate Blockage and its Mitigation by a Wettability Altering Nanofluid Treatment
Authors: Morgenstern Rico , Mukherjee Soujatya , Behr Aron , Rafiee Mohsen
Published: January 2023

During field development planning, the condensate blockage phenomena often receives minor attention leading to overestimation of the gas well deliverability. This study addresses the numerical analysis of both the risk potential of condensate blockage and the complex physiochemical processes of nanofluid treatment for two very different gas condensate fields.

Producing gas condensate reservoirs below the dew point results in condensate dropout and liquid holdup, which usually impairs the gas flow significantly. Wintershall Dea is currently investigating the feasibility of a nanofluid huff’n’puff treatment which aims to alter the wettability in the near-wellbore area, so that both water and condensate are repelled by the rock surface. For accurate representation of condensate blockage, Generalized Pseudo Pressure (GPP) along with local grid refinements (LGRs) were incorporated in the compositional model as well as high-velocity effects and a detailed post hydraulic fracture environment. The limitations of commercial simulation software in representing the nanofluid treatment in the dynamic reservoir model were overcome by combining the implementation of new hydrocarbon components, the surfactant model, tracers, chemical reactions, and special-purpose Python scripts.

The buildup and spatial distribution of the condensate in and around the well and the fracture was analyzed. The two key parameters influencing the severeness of condensate blockage comprise the composition of the reservoir fluid and the effective permeability of the gas phase, which are particularly influenced by multiphase flow behavior, formation and/or fracture damage, high-velocity flow effects, and subsurface equipment like gravel packs. In both reservoirs studied, wells were identified that will most likely experience significant productivity impairments due to condensate blockage, some already a few months after the start of production and some in the next few years.

The transport of the active nanofluid chemical was implemented by two different approaches: for one it was carried by a solvent system, and for the other it was dispersed as droplets in gas. Both approaches mainly differ in the depth of penetration and the propagation of the nanofluid as well as in the operational effort and the longevity of the wettability alteration. The nanofluid treatment cases demonstrated promising preliminary results with an increase in gas productivity index up to 30%.

The removal of condensate blockage using a novel wettability altering nanofluid formulation was initially conceptualized and subsequently analyzed for vertical wells and hydraulic fractures using numerical simulation. For the first time, a comparison between the injection of the active nanofluid within either a gas or solvent carrier was evaluated. The risk of condensate blockage and a potential treatment were analyzed for real case scenarios including promising business viability.
Rigorous Digital Rock Technology Validation through Cross-Sector Collaboration
Authors: Klemin Denis , Schlicht Peter , Distaso Ripalta Eleonora , Radaelli Francesco
Published: January 2023

Digital rock analysis (DRA) is a combination of experimental and numerical studies that helps to evaluate fluid transport and petrophysical properties at pore scale. We describe a concept of rigorous digital rock technology validation and present its workflow and application for a collection of reservoir rocks representing different lithologies, grain size and sorting, porosities, and permeabilities. The objective was to obtain relative permeability curves from a wide range of rocks (porosities ranging from 13% to 22% and absolute permeabilities from 3 to 570 md) using a direct hydrodynamic simulator that is based on the principles of density functional hydrodynamics and to verify the obtained results through a comparison with experimentally obtained relative permeability data.

Critical to the study was the separation between laboratory experimental procedures, performed by an energy company, and the digital experiments on the provided core imaging and fluid data performed by an energy technology service company. Each respective part of the study was performed independently, and data verification happened once both digital and physical data were obtained. The study was conducted in three phases. First, routine core analysis properties were established using DRA and experimentally. Second, a set of steady-state relative permeability digital experiments was performed using provided fluid data, flow rate, and P/T conditions that replicated experimental laboratory tests. Digital experiments were performed considering water-wet and mixed-wet scenarios. Third, a further sensitivity study compared basic (resolved meso- and micropore porosity) and advanced (resolved porosity plus submicron porous matrix and solid rock components) models to evaluate the effect of submicron porosity on multiphase transport properties.

All three phases were executed for each reservoir rock, and results were compared with independently obtained experimental data. An excellent match was observed on all studied rocks. Digital rock technology was proven to be a reliable technology for all studied rocks providing accuracy levels that are within the range of uncertainty of repeated laboratory test results on same core.

If applied in combination with the traditional laboratory analyses, DRA allows for a reduction of the cost and time for the petrophysical characterization of the rock and provides a better understanding of the transport mechanisms at the smallest scales.
New Insights into the Understanding of Capillary Trapping of Residual Oil in Carbonate Rocks Using Capillary Desaturation Curves
Authors: Siyal Amaar , AlAmeri Waleed , Al-Shalabi Emad W. , Ahmed Shehzad
Published: January 2023

Most of the oil remain trapped in the reservoir after both primary and secondary recovery stages. Enhanced oil recovery (EOR) techniques are usually implemented in the tertiary stage to recover the trapped oil. Accordingly, the inaccurate determination of residual oil saturation after waterflooding (Sorw) in the secondary stage affects the success and economics of the EOR processes in the tertiary stage. Thus, the capillary desaturation curve (CDC) is usually introduced as guidance to estimate the mobilized residual oil. The objectives of this study include determining the true Sorw for carbonate Indiana limestone outcrops under harsh conditions, then investigating the effect of trapping number, permeability, and initial oil saturation on Sorw, and finally characterizing the CDC for carbonate rocks by further reducing the Sorw using surfactant flooding. For this purpose, six carbonate Indiana limestone outcrop samples with different permeabilities (4-69 mD) and fluid samples i.e., field-representative oil, formation water, seawater, and surfactant solutions were utilized. The drainage process was performed systematically using a coreflooding system to establish initial water saturation by injecting heavy oil followed by crude oil and aging for two weeks. Afterward, all six cores were subjected to spontaneous imbibition using Amott cell. This was further followed by forced imbibition using both ultra-centrifuge and coreflooding systems for comparison purposes and achieving Sorw condition. Finally, forced imbibition was performed on all cores using coreflood to generate CDC using three different surfactants with varying IFT values.

The results showed that all rock samples achieved initial water saturation (Swi) in the range of 18-32% with no correlation between Swi and rock permeability. In addition, spontaneous imbibition tests showed slight oil production which reflect the oil-wetness of these cores used. It was noted that this slight production varied among cores with the same rock permeability range, which indirectly indicating the existence of heterogeneity within each permeability range. Furthermore, Sorw of 20-30% was reached using ultra-centrifuge and coreflooding method, indicating no correlation of permeability with Sorw. Based on the CDC studies, the critical trapping number was in the range between 10−5 and 10−4 for the tested cores, which is higher than the reported values in literature (10−8 to 10−6). This work provides a new insight into the understanding of capillary trapping effect on residual oil using CDC in carbonates. The complications in carbonate rocks, including the complex nature of high heterogeneity, mixed-to-oil wettability, high temperature, and high salinity, render accurate determination of true Sorw is a challenge at lab-scale. Sorw determination and CDC characterization aid in EOR screening to find the effective and economically viable methods for production enhancement.
Case Study Artificial Intelligence (AI) Assisted Numerical Reservoir Simulations in a Mature Reservoir Under Waterflood Development in the Sultanate of Oman
Authors: Shuaibi Fakhriya Abdullah , Hadhrami Munira Mohamed , Sheheimi Awadh Harbouq , Agarwal Binayak , Riyami Qassim Mohamed , Ruqaishi Mohammed , Habsi Naima , Mortezazadeh Emad , Mohajeri Sina
Published: January 2023

PDO is transforming its field development planning by adopting digital technologies and Artificial Intelligence (AI) to improve organizational efficiency and maximize business value through swift quality decisions and an evergreen forecast. In this context, the company has approached a number of third parties to bring in solutions in this domain. In 2021 one such collaboration with 3rd party contractor to test a novel solution involving data driven AI based dynamic simulator in a mature brown fiend setting. The objective was to test the tool, the efficacy and efficiency of the process, robustness and ease of use and its utility in current setting [1].

Existing dynamic modelling workflows with conventional simulators are extremely time consuming to update and upgrade in a mature brownfield setting. These conventional and lengthy iterative process of working might leave value on the table. It is time consuming to update history match with all the extra inputs and forecast; and optimizing the development with all the input parameters within a short timeframe is always a challenge.

The process employed in this approach was based on deep learning artificial neural networks (ANN) coupled with numerical simulators and along other static model inputs. The reservoir static and the flow dynamics were used as feature parameters to train the ANN, while the historical field production was used as the target parameters. The ANN training exercise identified the most contributed static and dynamic parameters to the historical production; therefore, these main parameters were given a higher weight in production forecasting and reservoir management.

This AI-simulation method was expected to be faster, data driven and allow a faster testing of multiple development strategies in short time.

This paper outlines the experience of an AI-assisted numerical simulation approach to unlock the potential of brown oil fields in south Oman by reducing the time spent on modelling and base case anchoring. It also enables evergreen forecasting by integrating AI techniques with numerical simulation. The AI-simulation was tried in a brown field with an existing FDP generated using conventional simulation tool where >50% of the FDP propose wells have been drilled. The outcomes from the AI-simulation result were compared with conventional simulation and with Actual field performance. Optimization was also conducted to locate the sweet spots for future drilling and WRFM opportunities. This optimized workflow has the potential to enable step change improvements in time and value for brownfield development and optimization for future developments.
Deriving New Type Curves through Machine Learning in the Wolfcamp Formation
Authors: Mask Gene Michael , Wu Xingru
Published: January 2023

Forecasting production from unconventional reservoirs is a slippery slope that could lead to unrealistic results even though a model has been history matched with production history and vetted by experienced engineers. The reasons for failing conventional decline curves lie in convoluting and heterogeneous reservoir properties, advancing drilling and completion techniques, and dynamic production and operations management. However, the initial rates, declining rate, and ultimate recovery of a well can be viewed as relatively static and predetermined properties of a declining profile. This paper will propose a machine learning-based framework to determine these properties for unconventional reservoir development.

In the proposed algorithm, instead of directly data mining on the raw data from different categories and scales, we propose to convert these data into dimensionless variable groups to reduce the dimension of the problem. The dimensionless variables are developed using inspection analysis; most have physical meanings and are easy to upscale. In the case study, we used the production, completion, and petrophysical data to generate new type curves and developed a step-by-step process to explain the aspect of "engineering" code that incorporates physics into the machine learning (ML) process.

Dimensionless variables are used in the machine learning process giving physical meaning and reducing the number of predictors, thus improving the speed and efficiency of the code. The results show that the quantity of cumulative oil production over time can be determined using machine learning models with R2 >= 0.90 for individual wells and R2>=0.80 for cross-validated cumulative production forecasts. We can use these determined values to assess the quality of initial rates, declining rates, and ultimate recovery to derive new type curves that incorporate physics and engineering practices. The work emphasizes the importance of accounting for completion parameters, fluid properties and rock quality, thus improving the confidence in results obtained through traditional engineering methods. The machine learning model results provide credibility and support to rates and recoveries for DCA forecasted wells. When modeling hundreds if not thousands of wells, this work shows the importance of utilizing machine learning to harness the power of the data that has been collected on them.

The machine-learning-based declining profile is a promising technique and has some advantages over the classical methods based on averaging historical data. First, the determining parameters are highly scalable for newly drilled wells as the main input parameters are dimensionless variables derived from reservoir properties and well completions. Secondly, this algorithm explores not only the production data but also reservoir properties and completion data to capitalize on the advancing techniques.
Reservoir Characterization and 3D Architecture of Multi-Scale Vugular Pore Systems in Carbonate Reservoirs
Authors: Zeiza Adam Danur , Almeshari Saleh Khaled , Mansor Abdulmohsin Saleh , Tarabbia Paul Joseph
Published: January 2023

This paper outlines a novel approach to an integrated 1D to 3D characterization and geomodeling of Vugular pore systems (VPS) in carbonate reservoirs. The study focused on capturing the various scales of the VPS and how they related to the 3D architecture.

An integrated approach for detecting, characterizing and modeling the multi-scale VPS has been employed by utilizing multi-disciplinary datasets, that span from 3D seismic volumes, borehole images, production data to petrographic analyses. These datasets were analyzed and corroborated in the 1D, 2D and 3D domains to validate and define the occurrence and architecture of the VPS within the reservoir. Eventually, a 3D geo-cellular model of the VPS is constructed by honoring diagenetic proxies and experiments, VPS flags, fluid flow behavior and seismic attributes geobodies.

VPS are observed at nearly all scales of datasets (i.e. geoscience and reservoir engineering data). They also occur at different scales of pore size ranging from millimeters to multi-meters. Many of them are even beyond the resolution of conventional whole core and basic well logs. A complete set of static and dynamic data allows a classification of the VPS into several classes based on their size, intensity and effect on reservoir properties and flow rates. Diagenetic proxies, well-based experimental variogram analyses and seismic-based geobody extraction further confirmed their architecture and distribution within 3D space. Their ramified patterns within the proximity of structural crestal areas tie quite consistently with well and seismic data. This architecture is further supported by possible hydro-dynamic corrosive fluid behavior that potentially had longer residence time in the crestal areas during late burial diagenesis stages.

A modeling-while-interpreting workflow is also configured to model the VPS in 3D interactively and confirm the VPS occurrence on a well-by-well basis. This method is applied directly to the 3D full-field model and is linked to an interpretation platform. This unique approach contributes to the reduction of ambiguity in subsurface data and analysis.