Reservoir Behavior Research Articles

Completion Performance Evaluation in Multilateral Wells Incorporating Single and Multiple Types of Flow Control Devices Using Grey Wolf Optimizer

There has been a tendency in oil and gas industry towards the adoption of multilateral wells (MLWs) with completions that incorporate multiple types of flow control devices (FCDs). In this completion technique, passive inflow control devices (ICDs) or autonomous inflow control devices (AICDs) are positioned within the laterals, while interval control valves (ICVs) are installed at lateral junctions to regulate the overall flow from each lateral. While the outcomes observed in real field applications appear promising, the efficacy of this specific downhole completion combination has yet to undergo comparative testing against alternative completion methods that employ a singular flow control device type. Additionally, the design and current evaluations of such completions are predominantly based on analytical tools that overlook dynamic reservoir behavior, long-term production impacts, and the correlation effects among different devices. In this study, we explore the potential of integrating various types of flow control devices within multilateral wells, employing dynamic optimization process using numerical reservoir simulator while the Grey Wolf Optimizer (GWO) is used as optimization algorithm. The Egg benchmark reservoir model is utilized and developed with two dual-lateral wells. These wells serve as the foundation for implementing and testing 22 distinct completion cases considering single-type and multiple types of flow control devices under reactive and proactive management strategies. This comprehensive investigation aims to shed light on the advantages and limitations of these innovative completion methods in optimizing well and reservoir performance. Our findings revealed that the incorporation of multiple types of FCDs in multilateral well completions significantly enhance well performance and can surpass single-type completions including ICDs or AICDs. However, this enhancement depends on the type of the device implemented inside the lateral and the control strategy that is used to control the ICVs at the lateral junctions. The best performance of multiple-type FCD-based completion was achieved through combining AICDs with reactive ICVs which achieved around 75 million USD profit. This represents 42% and 22% increase in the objective function compared to single-type ICDs and AICDs installations, respectively. The optimal settings for ICD and AICD in individual applications may significantly differ from the optimal settings when combined with ICVs. This highlights a strong correlation between the different devices (control variables), proving that using either a common, simplified analytical, or a standard sequential optimization approach that do not explore this inter-dependence between devices would result in sub-optimal solutions in such completion cases. Notably, the ICV-based completion, where only ICVs are installed with lateral completion, demonstrated superior performance, particularly when ICVs are reactively controlled, resulting in an impressive 80 million USD NPV which represents 53% and 30% increase in the objective function compared to single-type ICDs and AICDs installations, respectively.

Analyzing the impact of geological features on reservoir heterogeneity using heterogeneity logs: A case study of Permian reservoirs in the Persian Gulf

Carbonate reservoir heterogeneity has a major impact on fluid flow and hydrocarbon recovery. It is challenging to characterize due to the complex and heterogeneous nature of carbonate reservoirs. It is important to express heterogeneity as a quantitative parameter for comparing various reservoirs. It can also be used for investigating the influence of different factors on the degree of heterogeneity. Heterogeneity log is one of the effective methods for quantifying heterogeneity. The aim of the present study is to investigate the application of heterogeneity log to determine the geological features affecting heterogeneity in Upper Dalan Member (Late Permian) in the central Persian Gulf. The gamma ray, bulk density, neutron porosity, acoustic, and deep resistivity well logs data were used to calculate the heterogeneity logs. A total of 1093 thin sections were studied and primary depositional as well as secondary diagenetic changes were recorded. Porosity and permeability of the same number of samples were measured on the plugs. Heterogeneity logs were calculated by Lorenz coefficient method. The changes in heterogeneity logs were classified into different ranges and compared with frequency of geological features in corresponding intervals. Findings show that heterogeneity logs can help identify areas of the reservoir with distinct reservoir behavior (permeability and/or porosity). These areas can be targeted for increased oil and gas production. This can help reduce exploration and development costs. Dolomite samples are observed in both low and high heterogeneity values. Anhydrite lithology reduces porosity and increases heterogeneity. High heterogeneity values correspond with mudstone texture from supratidal, peritidal, and lagoon subenvironments with low porosity. Grainstone textures from shoal subenvironment and crystalline carbonates are associated with low values of heterogeneity. These microfacies have high porosity and permeability as well as a high rate of marine cementation. Also, interparticle and moldic pores along with stylolites and fractures are observed in low values of heterogeneity. The increase in the ratio of pore space to the solid component of samples has resulted in a reduction in the value of heterogeneity. By creating different subenvironments with different microfacies and average porosities, environmental energy controls the heterogeneity of the studied member. Therefore, the amount of heterogeneity is mainly controlled by two factors namely, porosity and pore type.

Data Science Has Exploded Across the Industry Over the Past 75 Years

In 1957, the Journal of Petroleum Technology published an article titled “Application of Large Computers to Reservoir Engineering Problems.” That was the first reference in JPT to what became known as supercomputers. The high-speed computers used in the 1957 article were capable of performing 60 million operations in about 3½ hours. They were proposed to analyze the thorny problem of multiphase flow. “It presently appears that the large computer will be required to investigate multiphase flow and to predict the flow behavior of oil and gas reservoirs considering two- and three-space dimensions.” Now, 67 years later, supercomputer speeds are measured in trillions of operations per second, and investigating multiphase flow is just one of the many uses. This extreme growth in speed has given rise to artificial intelligence (AI) and machine learning (ML), which has found its way into almost every corner of the petroleum industry. The Society of Petroleum Engineers and JPT has kept up with the digital advances in the industry, holding countless conferences, symposia, and other meetings centered on the digital aspects of the industry and recently creating the Data Science and Engineering Analytics (DSEA) technical discipline. In 2019, SPE launched the Data Science and Digital Engineering in Upstream Oil and Gas online publication. Four leaders in the oil and gas digital space shared their views on the current state of data science in the industry and the future of the discipline. Sushma Bhan, currently on the board of directors for Ikon Science, is SPE’s Technical Director for DSEA. Before joining Ikon, she worked for Shell for 32 years, eventually rising to the role of chief data officer for subsurface and wells. “My journey in the oil and gas industry began in 1988, when I joined Shell’s Production Computing Assisted Operations team as a programmer analyst,” Bhan said. “During my time there, I gained valuable exposure to field production operations and developed a deep understanding of real‑time data.” Jim Crompton, who claims he is mostly retired, is an associate professor of petroleum engineering at the Colorado School of Mines, a faculty fellow at the school’s Payne Institute for Public Policy, and director of Reflections Data Consulting. “My academic career started out in exploration geophysics, so my first introduction to engineering analytics came from processing seismic data at Chevron Geophysical Company in Houston,” Crompton said. “I studied earthquake seismology in graduate school, but, when I graduated, I learned the oil and gas industry paid a lot more than the USGS did, so my career goals changed.” Shahab D. Mohaghegh is a professor at West Virginia University and the president of Intelligent Solutions. “Since I started working for the petroleum industry throughout the world using artificial intelligence in 2000, I was able to work with actual data (field measurements) that has been saved by all the companies,” Mohaghegh said. “Working with actual data using AI provided several technologies that have been incredibly fantastic compared to what we have done in the past.” Pushpesh Sharma, the chair of the DSEA Technical Section, holds a PhD degree in chemical engineering from the University of Houston and is senior product manager for Aspen Technology. “Till a few years ago, the focus was on proving the efficacy of data science/machine learning methods for energy use cases,” Sharma said. “However, in recent years, the concerns are around large-scale deployment, maintenance, and the black‑box nature of ML models. Because of that, I started seeing increased focus on the deployment, explainability, and trust of ML models.”

Study of fracture monitoring and heat extraction evaluation in geothermal reservoir modified by abandoned well pattern: Numerical models and case studies

The abandoned well pattern for transforming geothermal reservoirs aligns seamlessly with the prevailing shift towards low carbon and enhanced energy efficiency. The effect of anomalous fractures on the effectiveness of heat production is not clear. In this study, we introduce the modified pressure build-up (MPBU) technology tailored for monitoring fracture behavior in geothermal extraction reservoirs. We construct numerical models for geothermal extraction, accounting for dynamic and irregular fractures. Results indicate that the MPBU technology substantially improves the precision of fracture model selection and parameter matching. The interpreted parameters are incorporated into the heat transfer model to assess reservoir heat distribution after 5000 days of extraction. The heat extraction effect of hydraulic fractures is similar to that of irregular fractures. Dynamic fractures have the worst heat extraction effect. Non Sorting Genetic Algorithm II is used to evaluate optimal monitoring and reservoir modification schemes. The innovative technology, numerical models, and optimal solutions will result in an economic improvement of $723,000 per well per year. In conclusion, the integrated monitoring and simulation approach proposed in this paper facilitates efficient and cost-effective differentiation of fracture states, thermal energy extraction evaluation, and optimization of the best extraction strategies to maximize economic benefits for engineers.

Implications of the depositional and diagenetic attributes on the reservoir properties of the siliciclastic mangahewa formation, Taranaki Basin, New Zealand: Integrated petrographical and petrophysical studies

The siliciclastic Mangahewa reservoir in the Pohokura Gas Field presents a formidable challenge for gas exploration due to its diverse petrophysical properties. To address this challenge, our study employs a comprehensive approach integrating petrography and petrophysical analyses for core and well logging data. In the present study, we uncover an association of sandstone microfacies, in the key wells like Pohokura South-1, Pohokura-1, and Pohokura-2 wells, which change from fine to medium-grained subfeldsarenites to lithic feldsarenites microfacies. The petrographic investigation pinpoints the critical factors influencing the reservoir quality, including facies types, grain size, pore types, sedimentary structure, detrital clay content, and mineral composition. Both the primary intergranular and secondary dissolution pore spaces are responsible for the influential large intergranular pore volumes causing a superior reservoir quality. Mineral composition, influenced by the source and lithofacies, significantly impacts the reservoir properties. Diagenetic processes further control the reservoir quality, where the abundant silica cement, the illitic clays, and the rare pervasive carbonate cementation render the low reservoir quality. Compaction significantly reduces porosity during diagenesis. High-quality reservoir sandstones are characterized by coarse grain size, minimal detrital clay presence, predominantly quartz composition, limited carbonate and moderate silica cementation, low authigenic illite levels, and minor compaction impact. Based on the petrophysical data, the Mangahewa reservoir is composed of five RRTs (reservoir rock types) with the RRT1 samples having the best reservoir characteristics due to their fair porosity, very good permeability, and macro pore sizes. On the other side, the RRT5 samples are characterized by the lowest reservoir quality (poor porosity and permeability and micropore sizes) due to cementation and compaction. Our study provides valuable insights into the intricate petrophysical properties of the Mangahewa reservoir, enhancing the predictability of the reservoir behavior and optimizing hydrocarbon production in this challenging geological setting.

Long-term study of phytoplankton dynamics in a supply reservoir reveals signs of trophic state shift linked to changes in hydrodynamics associated with flow management and extreme events

This study analyses over a decade (2009–2022) of monitoring data to understand the impact of hydrological characteristics on water quality and phytoplankton dynamics in Prospect Reservoir, a critical water supply for Greater Sydney, Australia, known for its excellent water quality. Water quality and phytoplankton dynamics were related to hydrodynamics, linked to flow management and the water quality of inflows. Phytoplankton biovolume increased after a prolonged drawdown and subsequent refill event, mainly driven by dinoflagellates, and corresponded to increases in total phosphorus and water temperature. The hydrological period following the 2019/2020 summer bushfires (post-bushfire) that impacted connected reservoirs, was marked by increased flow activity and nutrient loading, leading to significant shifts in the phytoplankton community. Functional group classification and ordination analysis indicated a transition from taxa typically dominant in oligotrophic conditions to meso‑eutrophic. This transition correlated with elevated nutrient levels and chlorophyll-a (Chl-a), and reduced Secchi depth and dissolved oxygen, providing evidence of eutrophication. Q index indicated good water quality post-bushfire, contrasting with a eutrophic status assessment using Chl-a. Our findings highlight the importance of analysing long-term datasets encompassing varied hydroclimatological conditions for a deeper understanding of reservoir behaviour. A comprehensive approach to water quality assessment is recommended, combining functional group classification, Q index and Chl-a measurements for effective reservoir health assessment. This research provides novel insights into the effects of disturbances such as bushfires, on water quality and phytoplankton dynamics in an underrepresented geographic region, offering valuable knowledge for managing water resources amidst growing climate variability.

Unlocking Geothermal Energy: A Thorough Literature Review of Lithuanian Geothermal Complexes and Their Production Potential

Lithuania is located on the East of Baltic sedimentary basin and has a geothermal anomaly situated in the southwestern region of the country. There are two primary geothermal complexes within the anomaly, composed of Cambrian and Devonian aquifers. The Cambrian formation is composed of sandstones that have a reservoir temperature reaching up to 96 °C (depth > 2000 m). The Devonian aquifer is composed of unconsolidated sands of Parnu–Kemeri and has a reservoir temperature of up to 46 °C (depth > 1000 m). Historically, both formations have been investigated for geothermal energy production. In this article, we present a detailed literature review of the geothermal work carried out on both formations, including past, present, and some possible future studies. The study presented in this paper highlights the key findings of previous research work, summarizes the research gaps, and then elaborates on the possible applications of emerging technologies to bridge the research gaps and improve our understanding of geothermal complexes in Lithuania. Although it is not the main aim of this article, this article also touches upon the important need to develop 2D/3D numerical models, to quantify uncertainties, in the evaluation of the geothermal potential in Lithuania for commercial development. This study also highlights possibilities of extending geothermal development to depleted hydrocarbon reservoirs through repurposing the high-water-production wells. Moreover, from the literature review, it can be concluded that the Lithuanian geothermal aquifers are hyper-saline in nature and temperature changes lead to the deposition of salts both upstream and downstream of the reservoir. Therefore, there is a need for developing multiphysics thermo-mechanical–chemical (THMC) models for evaluation of reservoir behavior. The literature also describes the potential use and development of the THMC model as a part of future work that must be carried out.

Hydromechanical simulation of a carbonate petroleum reservoir using pseudo-coupling

In the domain of petroleum reservoir management, numerical flow simulation stands as the primary tool for prediction and control. Traditionally, rock compressibility has been the key geomechanical parameter, often applied as a single, unchanging value across the entire reservoir model throughout the simulation. This study introduces a novel methodology that enhances reservoir simulation accuracy by incorporating rock mechanical behavior through a pseudo-coupling approach, without increasing computational costs. This approach empowers reservoir engineers to consider rock hydromechanical behavior in daily reservoir studies.The pseudo-coupling updates both porosity and permeability based on tables that correlate pore pressure to porosity and permeability multipliers, thereby replacing the conventional reliance on compressibility. It begins with reservoir-rock samples selection guided by lithological composition, followed by mechanical and hydromechanical laboratory tests, and numerical adjustments with ABAQUS stress analysis software. The resulting mechanical parameters serve for generating pseudo-coupling tables tailored to each facies.Validation experiments comparing pseudo-coupling simulations with conventional and fully coupled models reveal convergence in porosity and permeability variations with pore pressure between both coupled approaches. Extensive assessments further compare pseudo-coupling simulations against conventional reservoir simulations using a synthetic model based on a carbonate post-salt reservoir, denoted as Field B. The pseudo-coupling approach emerges as a more robust model capable of discerning preferential flow paths, all while maintaining computational efficiency. Moreover, these preferential flow paths were also predicted by the reservoir team responsible for the field development, but through a trial-and-error procedure to match production history, whilst the pseudo-coupling predicted it based on rock mechanical behavior.In this way, the proposed methodology enables an enhancement in the accuracy and efficiency of petroleum reservoir management by considering geomechanical effects, without incurring any additional computational costs when compared to conventional reservoir simulation.

An Explicit Data-Driven Model for Estimation of Free and Absorbed Gas Volumes in Shale Gas Reservoirs.

Shale gas reservoir fluid flow is characterized by complex interactions between a number of variables, including stress sensitivity, matrix shrinkage, and critical desorption pressure. The behavior and productivity of shale gas reservoirs are significantly influenced by these variables. Therefore, in this study, an in-house finite element poro-elastic simulator was integrated with an in-house Fortran-coded artificial neural network model for accurate estimation of free and absorbed gas volumes in shale gas reservoirs, taking into account the combined effects of stress sensitivity, matrix shrinkage, and critical desorption pressure. The in-house finite element simulator was used to create a database of free and absorbed gas volumes under different reservoir, fluid, and geo-mechanical characteristics of shale gas, while the in-house neural network code was utilized using the outcomes of the poro-elastic simulator in order to propose an explicit data-driven mathematical formula of free and absorbed volumes of shale gas. The study findings indicate that the neural network model achieved high accuracy with an R-squared value exceeding 99% during both the training and blind-testing phases. The newly developed ANN-based correlations can be used as a quick and easy-to-use tool to forecast the volume of shale gas in place both free and adsorbed without any prior experience with utilizing or implementing machine learning models.

Study on time-varying characteristics and flow behavior of microencapsulated polymer

Due to the challenges posed by high injection pressure and significant viscosity shear loss, conventional polymer flooding methods are unable to effectively meet the urgent demand for enhanced oil recovery in medium- and low-permeability reservoirs. Microencapsulated polymer flooding is a promising solution to address the aforementioned challenges due to its advantages of easy wellbore injection, shear resistance, and delayed thickening. However, there is a lack of research on the release behavior and time-dependent characteristics of microencapsulated polymer and its impact on flow behavior in reservoirs. To address this gap, we conducted a series of experiments, including thermal stability tests, microfluidic chip experiments, and micro-scale oil displacement experiments, to reveal the time-varying behavior generated by the triggered release of microencapsulated polymers. The results showed that microencapsulated polymer can be fully triggered after 8 days of aging at 85 °C, with a viscosity exceeding the initial viscosity by more than 25 times. The release mechanism of the polymer aligned with the Ritger-Peppas model, transitioning from super Case-II transport to anomalous transport, and finally to Fickian diffusion with increasing temperature. During the triggering process, the presence of particles stabilized shear and improved oil displacement, particularly in small pore throats. Higher triggering degrees of the solution resulted in greater flow resistance within the microchannel, effectively suppressing fluid fingering. The oil displacement effectiveness of fully triggered microencapsulated polymer was comparable to that of conventional polymers. This study provides insights into the flow behavior of microencapsulated polymer at a microscopic level, offering valuable references for its application in the field.

Technology Focus: Production Monitoring (March 2024)

The oil and gas industry is leveraging technology to increase competitiveness while addressing sustainability goals, supply-chain disruptions, and regulatory standards. During the past 3 years, over half a million patents were granted for production monitoring. This progress has led to a shift toward integrated work flows, cloud-based platforms, resilient sensors, process simplification, and automation. These advancements aim to enhance reservoir understanding, simplify monitoring programs, and reduce costs. Next-generation technologies are being progressively implemented to automate processes, modernize operations, and boost efficiency and production while meeting sustainability requirements. Data-set integration is becoming the key for comprehensively understanding reservoir behavior, which, in combination with the use of artificial intelligence and hybrid modeling for production analysis, results in faster informed decision-making. The following are some examples of advances in the past year: - Increased use of cloud-based platforms providing real-time data access, data visualization, analysis, and modeling and enabling remote operations - New sensors with improved reliability able to operate in previously inaccessible environments showcased with promising results (e.g., clap-on ultrasonic electromagnetic multiphase meter, sensor ball) - Downhole monitoring with tracers used to support well-cleanup operations and increase zonal productivity - Innovative solutions to gather flow-profile information in challenging areas, such as the use of distributed temperature and acoustic sensing with thermal modeling in high-temperature wells - Crosswell strain and production-profile interference testing in the Permian region, offering a holistic approach to reservoir monitoring through insights into fracture network dynamics at low cost and low risk - New rapid solutions for production analysis from multitransverse wells providing key fracture and reservoir properties - Advanced logging tools, such as the flow array sensing tool, successfully used in complex wells with high gas-to-liquid ratios Overall, these advancements have improved our understanding of reservoir behavior, optimized production efficiency, and contributed to the goal of achieving a sustainable energy future.

Analysis of the Effect of Coordination Number on Permeability of the Three-Dimensional (3D) Rock Model Using the Lattice Boltzmann Method (LBM)

Understanding the correlation between coordination number and permeability is crucial for predicting fluid flow behavior in hydrocarbon reservoirs. In this study, we investigated the influence of coordination number on permeability in three-dimensional (3D) rock models. The research methodology involved the creation of synthetic 3D rock models, incorporating pores networks with varying coordination numbers. Utilizing the Lattice Boltzmann Methods (LBM), a computational fluid dynamics approach, we simulated fluid flow through these synthetic rock models and quantified their permeability. Our findings demonstrated a strong dependency of permeability on the coordination number of synthetic rock models. The application of the Lattice Boltzmann Method (LBM) proved to be an effective tool for understanding fluid flow behavior in porous rock formations and can serve as a basis for further optimization of reservoir management strategies to maximize hydrocarbon exploitation.

Numerical Simulation Study on the Effect of Preinjected CO2 on the Hydraulic Fracturing Behavior of Shale Oil Reservoirs.

Due to the low permeability and apparent mechanical anisotropy of shale reservoirs, shale oil production highly depends on the performance of hydraulic fracturing of the tight reservoir. Pre-injection of CO2 before hydraulic fracturing treatment has been proven beneficial to enhance shale oil production. A comprehensive understanding of the effect of changes in the properties of shale reservoirs after preinjected CO2 on the hydraulic fracturing behavior of shale reservoirs is essential to improve the stimulated reservoir volume (SRV) of shale oil reservoirs. In this study, comprehensive evaluating parameters were proposed to specify the variation of mechanical properties of shale rock at different soaking times of CO2 based on published testing parameters of shale. Accordingly, a three-dimensional (3D) numerical model was established and three groups of horizontal well fracturing cases with different cluster spacings were conducted based on the adaptive FE-DE method to simulate and compare the hydraulic fracturing behavior in the reservoirs with different mechanical properties. We established a quantitative relationship between the alterations in reservoir properties and the stimulated reservoir volume. The results indicate that both the brittleness and conductivity properties of shale rock are dramatically improved as the increment of soaking time of CO2. It is beneficial to improve the SRV, and the initiation pressure is reduced with the increment of soaking time of CO2. However, as the stress shadow effect is involved in the horizontal well fracturing, the complexity of the hydrofracturing crack is significantly enhanced to restrain the development of hydrofracturing crack. When the cluster spacing is larger, the stress shadow effect is weakened, and the weakening effect of CO2 soaking on reservoir is more obvious.

A New Method for Calculating the Influx Index in Gas-Drive Reservoirs: A Case Study of the Kela-2 Gas Field

The calculation of the influx index is one of the most contentious issues in dynamic reserve evaluation of gas reservoirs’ development. For the influx index, it is key to obtain information on the pore compressibility coefficient under realistic gas reservoir pressure. So far, little is known about the assessment of the pore compressibility coefficient at a laboratory scale. Here, we combine observations of gas flowmeter, ISCO booster pump, intermediate container, and rock samples to quantify the pore compressibility coefficient from the KL2-13 well in the Kela-2 reservoir. Additionally, the iterative method (combined the static and dynamic methods) is proposed based on the experimentally obtained pore compressibility coefficient (Cf), dynamic reserve (G), water body multiple (β), and material balance equation to calculate the influx index. The combined iterative method adjusts the values of G and N by comparing the results of the static and dynamic methods, and iteratively corrects Cf using a binary search method until the results of the static and dynamic methods are consistent. The results of our study reveal that the influx index calculated by the dynamic and static methods was consistent, and the gas production per unit pressure drop matched the actual production. These results strongly suggest that there exists a correlation between formation pressure and the influx index, wherein the latter exhibits a gradual decrease as the former decreases. Conversely, the displacement index of both the rock and connate water do not demonstrate a significant dependence on pressure. Furthermore, the impact of pressure on the pore compressibility factor and reservoir water compressibility factor appears to be minimal. These findings hold substantial implications for understanding the behavior of gas reservoirs under varying pressure conditions.

Multi-phase behavior and pore-scale flow in medium-high maturity continental shale reservoirs with Oil, CO2, and water

The application of CO2 to enhancing the recovery of continental shale oil has attracted considerable attention. However, the introduction of CO2 leads to intricate phase behavior and flow mechanisms. In this study, firstly, a three-phase equilibrium model is developed, with nano-confinement effects taken into consideration, to analyze the phase behavior of continental shale oil-CO2-water systems in both a bulk phase and nanopores. Building upon this, an improved pore network model is introduced to simulate steady-state oil–water flow while considering the alterations in fluid properties resulting from CO2 dissolution. The findings reveal that with the increase of CO2 injection, a transition from two phases of liquid and water to two phases of vapor and water, three phases of liquid, vapor and water, two phases of vapor and water, and ultimately a pure vapor phase occurs. The dissolution of CO2 exerts a pronounced impact on the composition and properties of the oil phase, and the viscosity and interfacial tension reduction effects play a central role in affecting the flow capacity. Nano-confinement effects facilitate the transfer of vapor-phase molecules into the water and oil phases, thus increasing the concentration of CO2 in the oil and water phases. The pore-scale flow results reveal that upon injection, water quickly traverses the larger pores. More smaller pores become accessible after CO2 dissolution, significantly boosting the extraction of shale oil from these smaller pores and slightly reducing the water output.

Effect of wax content on phase behavior of ultra-deep condensate gas reservoir: Experimental and thermodynamic model

The development of ultra-deep oil and gas reservoirs has become a trend in recent years. Comprehensive understanding of the complex phase behavior of waxy condensate gas is necessary for the exploitation of ultra-deep gas reservoir. The objective of this work is to study and predict the phase behavior of condensate gas under various wax contents. The condensate gas and wax samples were collected from ultra-deep condensate gas well in northwestern China. The phase behavior tests were performed to determine the dew point pressure, percentage of condensate liquid, and compressibility factor of condensate gas under different wax contents using an ultra-high-pressure Pressure-Volume-Temperature (PVT) setup. It can be found that the dew point increased with the increasing wax content. The percentage of condensate liquid and compressibility factor increased with the increasing wax contents. A thermodynamic model based on an equation of state (EOS) was proposed to describe the phase behavior under different wax content. The average absolute deviations (AAD%) between the calculated compression factor and the experimental values of 0 %, 6.79 %, 10.86 % and 14.18 % wax contained are 3.73 %, 2.43 %, 0.76 % and 0.25 %, respectively. The results show that the thermodynamic model developed in this work exhibits the highest prediction accuracy, which can provide the necessary guidance for the development of ultra-deep waxy condensate gas reservoir.

Numerical simulation of a nonlinear diffusion type equation in a two phase media with linear porosity and permeability model

Predicting and understanding the behavior of pressure is important in reservoir maintenance and evaluation. This work studies the behavior of fluid pressure in a reservoir by numerical simulation of the pressure diffusion type equation in a two phase media with a linear porosity and permeability model. Because the porosity and permeability are pressure dependent, the resulting diffusion type equation is nonlinear and is solved using a backward-forward finite difference method. The simulation code was ran using a constant porosity and permeability model and a linear porosity and permeability model. The results from the linear porosity and permeability model was compared to that of the constant porosity and permeability model. In both cases the pressure gradient was greatest at the wellbore and decreases as the radial distances away from the wellbore increases. The pressure in both cases also decreased with time. However, at each location and time the pressure drop was lower in the linear porosity and permeability model than with the constant porosity and permeability. The backward-forward finite difference method proved to be useful in solving numerically the nonlinear diffusion type equation. This work can be applied in the oil and gas industry to predict pressure behavior in reservoirs and make investment decisions, production and maintenance decision.

Machine Learning Provides Fracture Analysis, Mapping for CCUS

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 214996, “Machine-Learning Application for CCUS Carbon Storage: Fracture Analysis and Mapping in the Illinois Basin,” by Guoxiang Liu, SPE, US Department of Energy, and Abhash Kumar and William Harbert, SPE, Contractors, et al. The paper has not been peer reviewed. _ The monitoring of storage reservoirs to ensure safe, long-term storage of CO2 and to derisk operations and storage management is undergoing dynamic shifts, expanding opportunities for implementing innovative techniques and applications, especially for commercial-scale deployment. In the complete paper, a method with multitiered analysis has been developed to leverage advanced machine-learning (ML) techniques to process passive seismic monitoring data acquired during an injection period along with pumping/injection pressure and rate in the Illinois Basin for potential fracture and fault analysis. Introduction This study is part of the Science-Informed ML for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative funded by the US Department of Energy Carbon Storage Program. The outcomes of the SMART Initiative are science-informed, ML-based tools that can be applied at carbon storage sites throughout the nation and the world to achieve the following objectives: - Improve the ability to consolidate technical knowledge, site-specific characterization information, and real-time data in a digestible way - Enable the optimization of carbon-storage reservoirs by creating a capability for real-time forecasting of the behavior of such reservoirs - Improve the ability to understand and communicate subsurface behavior during carbon-storage operations to nonexperts Methodology In the authors’ study, a multitiered, data-driven approach was created by integrating all available measurement data to visualize fracture networks. The data sets included measurements from drilling, log and core testing, injection and downhole pressure measurements, and five vertical-pressure-monitoring gauges, providing a wealth of detail on the fracture networks. A fracture network encompasses natural fractures, induced or hydraulic fractures, and the dynamic response of such fractures, culminating in a comprehensive understanding of this system. Because of the wide range of resolutions and scales of the data sets, the fracture network was mapped into 19 distinct time windows over a 3-year injection period by analyzing pumping data, which included CO2 injection and bottomhole pressure. Passive seismic data (microseismic) were compartmentalized into these time windows, similar to the industry practice of compartmentalizing microseismic data into separate stages for high-pressure fluid- injection activities (i.e., induced fracturing). In each of these time windows, the b-value of the associated microseismic population was estimated. Furthermore, event-occurrence time and distance from the treatment well were used to identify the distinct triggering front of microseismicity likely associated with pre-existing fractures and faults within the reservoir. Seismogenic b-value and diffusivity analyses were applied to identify triggered fronts for further clustering and fracture-plane and 3D fracture-distribution analysis. The potential of several unsupervised ML algorithms was leveraged to identify the spatial clusters of microseismic events within each triggering front of individual time windows by following the work flow described in Fig. 1 of the complete paper. Furthermore, these ML techniques were implemented to determine the best-fitting surface for each spatial cluster of microseismicity to infer the directional and spatial distributions of fractures.

Study on Rock Mechanics Parameter Prediction Method Based on DTW Similarity and Machine-Learning Algorithms

Rock mechanics parameters are crucial factors for predicting rock behavior in oil and gas reservoirs, optimizing extraction strategies, and ensuring drilling safety. In this study, we propose a random forest (RF)-convolutional neural network (CNN)-long-term short-term memory network (LSTM) fusion model based on the dynamic time warping (DTW) algorithm to construct intelligent prediction models for elastic modulus, Poisson’s ratio, and compressive strength using real-time drilling engineering data. An autoencoder with a sliding window is employed to automatically identify abnormal points or segments in the calculated values of elastic modulus, Poisson’s ratio, and compressive strength obtained from drilled wells. These abnormal values are then corrected using a backpropagation (BP) neural network. Compared to single CNN-LSTM or single RF models, the RF-CNN-LSTM fusion model performs better. It achieves this by effectively combining the strengths of different algorithms in predicting outcomes. The accuracy of the RF-CNN-LSTM fusion model is over 94% when compared to the actual values. Furthermore, the analysis of the relative importance of input parameters reveals that weight on bit (WOB), temperature, displacement, equivalent circulation density (ECD), and mud density are the primary input features for predicting elastic modulus. For predicting Poisson’s ratio, the main input features include WOB, mud density, ECD, temperature, pumping pressure, displacement, and rate of penetration (ROP). Similarly, for predicting compressive strength, the main input features consist of WOB, temperature, displacement, ECD, and mud density. The research findings demonstrate that the rock mechanics parameter prediction models based on the RF-CNN-LSTM algorithm using DTW exhibit high computational accuracy in the B oil field of China. These results are significant for gaining a deeper understanding of the variations in rock mechanics parameters and optimizing drilling decisions.

Characterization of onshore hydrocarbon reservoirs in the Amazonas Basin using well logging data

Reservoir characterization is extremely important for understanding and determining the behavior of hydrocarbon reservoirs. The geophysical well logging, widely applied in the oil industry, helps to obtain essential physical properties (acoustic, electrical, mechanical, radioactive and thermal) by deploying measurement devices in the drilled well. This project aims to carry out the geological characterization of hydrocarbon reservoirs in the Amazonas Basin through the analysis of well geophysical logs. Despite the oil-producing potential of the basin, there are few published papers that use geophysical well logging for reservoir characterization. For this study, composite log data publicly provided by ANP were used. Six exploratory wells drilled in AM-T-84 and AM-T-85 exploratory blocks in the Amazonas Basin were selected. By analyzing the geophysical logs of gamma ray, resistivity, density and neutron from these wells, it was possible to identify the hydrocarbon reservoir interval within the Monte Alegre formation and obtain estimates of thickness, shale volume and porosity. The identified reservoir is relatively thin, with an average thickness of 16 m. The obtained shale volume values were relatively high, due to the influence of shale layers present in the Monte Alegre formation. The effect of clay content affected the interpretation process of the well logs; however, it did not significantly impact the reservoir porosity estimation, which ranged between 10.4 and 23.4 %.