Improved Reservoir Management Research Articles

The Impact of Condensate Oil Content on Reservoir Performance in Retrograde Condensation: A Numerical Simulation Study

This study investigates the complex dynamics of retrograde condensation in condensate gas reservoirs, with a particular focus on the challenges posed by retrograde condensate pollution, which varies in condensate oil content and impacts on reservoir productivity. Numerical simulations quantify the distribution of condensate oil and the reduction in gas-phase relative permeability in reservoirs with 100.95 g/m3, 227.27 g/m3, and 893.33 g/m3 of condensate oil. Unlike previous studies, this research introduces an orthogonal experiment to establish a methodology for studying the dynamic sensitivity factors across different types of gas reservoirs and various development stages, systematically evaluating their contributions to condensate oil. The analysis reveals that reservoirs with low to moderate condensate oil content gradually experience expanding polluted regions, affecting long-term production. The maximum condensate saturation near the wellbore can reach 0.19, reducing gas-phase relative permeability by about 25.44%. In contrast, high-condensate oil reservoirs show severe early-stage retrograde condensation, with saturations up to 0.35 and a permeability damage rate reaching 73%. The orthogonal experiments identify reservoir permeability and condensate oil content as critical factors influencing production indicators. The findings provide key insights and practical recommendations for optimizing production strategies, emphasizing tailored approaches to mitigate retrograde condensation and enhance recovery, especially in high-condensate oil reservoirs, offering theoretical and practical guidance for improving reservoir management and economic returns.

The concept of big data and predictive analytics in reservoir engineering: The future of dynamic reservoir models

Reservoir engineering is a critical discipline in the oil and gas industry, aimed at optimizing the extraction and management of subsurface resources. Traditionally, reservoir models have relied on static data and conventional simulation methods, which often fall short in capturing the dynamic complexities of reservoirs. With the advent of big data and predictive analytics, there is a significant opportunity to revolutionize reservoir modeling by integrating real-time data for continuous updates and more accurate predictions. This review introduces a theoretical exploration of how big data and predictive analytics can transform dynamic reservoir models, highlighting their potential to improve reservoir management and hydrocarbon recovery. Big data technologies enable the collection, storage, and processing of vast amounts of structured and unstructured data from multiple sources, including well logs, seismic surveys, drilling operations, and production data. Predictive analytics, through machine learning and statistical techniques, can extract actionable insights from these datasets to predict reservoir behavior, enhance decision-making, and optimize production strategies. This integration facilitates real-time monitoring and adaptive reservoir management, allowing engineers to respond to changing reservoir conditions and uncertainties more effectively. The review further explores the implications of big data-driven predictive models for enhanced hydrocarbon recovery techniques, such as water flooding and gas injection. By automating data processing and integrating real-time field data, predictive analytics can improve the accuracy of reservoir simulations, reduce downtime, and increase operational efficiency. Ultimately, the future of dynamic reservoir modeling lies in the seamless integration of big data and predictive analytics, providing a pathway to smarter, more sustainable resource management and maximized recovery factors in increasingly complex reservoir environments. Keywords: Big Data, Predictive Analytics, Reservoir Engineering, Dynamic Reservoir, Models.

Experimental study on the dynamic threshold pressure gradient of high water-bearing tight sandstone gas reservoir

Tight sandstone water-bearing gas reservoirs typically exhibit low porosity and low permeability, with reservoir rocks characterized by complex pore structures, often featuring micron-scale or smaller pore throats. This intricate reservoir structure significantly restricts fluid flow within the reservoir, necessitating a certain threshold pressure gradient (TPG) to be overcome before flow can commence. This study focuses on the Ordos Basin and explores the influence of high water content tight sandstone gas reservoirs on TPG under different water saturation and formation pressure conditions through experiments. A mathematical model of TPG is established using multiple linear regression method. The results show that TPG is primarily affected by water saturation, followed by formation pressure. As the water saturation increases, the TPG of the core decreases, and the change becomes more pronounced when the water saturation exceeds 50%. As formation pressure increases, the weakening of the slippage effect in gas molecules leads to TPG stabilization, especially when local pressure exceeds 25.0 MPa. The research also shows that low-permeability cores exhibit greater TPG variation with pressure changes, while high-permeability cores remain more stable. A mathematical model was developed and validated to predict TPG based on permeability, water saturation, and formation pressure. These findings highlight the need to monitor formation water content during tight sandstone gas reservoir development to optimize production strategies, providing valuable insights for improving reservoir management and guiding future research.

A comprehensive analysis of surface water changes in Dau Tieng Lake, Vietnam, across dry and wet seasons

This study investigates the seasonal dynamics of Dau Tieng Lake near Tay Ninh, Vietnam, employing remote sensing technology, specifically the Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Vegetation Index (NDVI). Focused on the period from 2016 to 2020, the research aims to detect changes in the water surface area during dry and wet seasons. As the largest reservoir for water supply and irrigation in Vietnam, Dau Tieng Lake faces operational challenges due to seasonal rainfall variations and increasing water demands. The results reveal significant differences in water body areas between wet and dry seasons, with MNDWI proving more effective in capturing these variations compared to NDVI. The study concludes that the seasonal change in Dau Tieng Lake is 21.76 km2 (NDVI) and 25.55 km2 (MNDWI), emphasizing the latter's superiority in accurately detecting water body changes. These findings contribute valuable insights for improved reservoir management and sustainable utilization of water resources in the region.

Technology Focus: Digital Data Acquisition (January 2024)

Digital data acquisition has revolutionized the oil and gas industry, offering unprecedented opportunities for efficiency, cost reduction, and informed decision-making. Our industry is at the forefront of a digital transformation, where the effective acquisition and use of data have become paramount to success amidst global pressure for energy transition. Recent industry trends have seen a significant shift toward the use of legacy data, the integration of various sources of data, and the application of machine-learning techniques, creating a more dynamic and data-driven landscape. Machine learning, a subset of artificial intelligence, has become a game-changer where massive data sets from various sources—including drilling, production, and reservoir data—are being analyzed to predict equipment failures, optimize drilling procedures, and improve reservoir management. Machine-learning models also can enhance safety by predicting and preventing accidents, thus reducing downtime and operational risks. Traditionally, oil and gas companies operated in departmental silos, with different teams managing their data sources independently. Recent industry trends emphasize the integration of various data sources to create a more holistic understanding of reservoirs and operations. The integration of well logs and seismic data, for example, provides a comprehensive view of subsurface conditions. By combining these data sources, operators can better understand geology and reservoir characteristics to improve exploration and development success. Furthermore, by continuously monitoring and analyzing reservoir performance using data from drilling, production surveillance, and interventions, operators can make timely adjustments to production strategies, maximizing hydrocarbon recovery and field lifespan. As technology continues to advance, the industry’s potential for further optimization and growth is boundless. Embracing these digital trends is not just a matter of staying competitive; it’s about ensuring a more sustainable and efficient future for the industry as a whole. Recommended additional reading at OnePetro: www.onepetro.org. OTC 32210 Pushing the Limits in Deepwater Data Acquisition for Accelerated Field Development: Industry Record Batch Well Testing by Ozgur Karacali, SLB, et al. OTC 32643 Evaluation of Loop Current/Loop Current Eddy Fronts To Guide Offshore Oil and Gas Operations by Jill Storie, Woods Hole Group, et al. SPE 211807 Multifrequency Data-Acquisition Model and Hybrid Neural Network for Precise Electromagnetic Wellbore Casing Inspection by Guang An Ooi, King Abdullah University of Science and Technology, et al.

Persistent response of the bottom free-living bacteria to typhoon events in a subtropical reservoir

Typhoon-induced perturbations can result in long-lasting effects on aquatic communities in subtropical lakes or reservoirs. However, the responses of bacterial communities and their related nutrient cycling to episodic typhoon events throughout the water column in deep waters remain largely unknown. Here, we conducted a four-year field study to reveal the depth-specific responses of both free-living (FL) and particle-attached (PA) bacteria to typhoon events in a subtropical deep reservoir from 2015 to 2018. By comparing the depth-specific responses of FL and PA bacteria, we found that typhoon-induced inputs of organic matter and microorganisms significantly increased FL bacterial diversity and changed FL bacterial community composition in bottom waters perhaps through the density current or undercurrent. Typhoon events had a more persistent effect on FL than PA bacterial communities, especially in bottom waters of the reservoir. Free-living bacteria were more associated with nutrient cycling in bottom waters than particle-attached bacteria. These findings provide deep understanding of how FL and PA bacteria respond to typhoon events at community level in subtropical deep reservoir and thus help us to improve reservoir management in a rapidly changing world.

Simulating Oil and Water Production in Reservoirs with Generative Deep Learning

Summary This study investigated the ability to produce accurate multiphase flow profiles simulating the response of producing reservoirs, using generative deep learning (GDL) methods. Historical production data from numerical simulators were used to train a variational autoencoder (VAE) algorithm that was then used to predict the output of new wells in unseen locations. This work describes a procedure in which data analysis techniques can be applied to existing historical production profiles to gain insight into field-level reservoir flow behavior. The procedure includes clustering, dimensionality reduction, correlation, in addition to novel interpretation methodologies that synthesize the results from reservoir simulation output, characterizing flow conditions. The insight was then used to build and select samples to train a VAE algorithm that reproduces the multiphase reservoir behavior for unseen operational conditions with high accuracy. Furthermore, using deep feature space interpolation, the trained algorithm can be used to further generate new predictions of the reservoir response under operational conditions for which we do not have previous examples in the training data set. It is found that VAE can be used as a robust multiphase flow simulator. Applying the methodology to the problem of determining multiphase production rate from new producing wells in undrilled locations showed positive results. The methodology was tested successfully in predicting multiphase production under different scenarios including multiwell channelized and heterogeneous reservoirs. Comparison with other shallow supervised algorithms demonstrated improvements realized by the proposed methodology. The study developed a novel methodology to interpret both data and GDL algorithms, geared toward improving reservoir management. The method was able to predict the performance of new wells in previously undrilled locations, potentially without using a reservoir simulator.

Establishing Stage–Discharge Rating Curves in Developing Countries: Lake Tana Basin, Ethiopia

A significant constraint in water resource development in developing countries is the lack of accurate river discharge data. Stage–discharge measurements are infrequent, and rating curves are not updated after major storms. Therefore, the objective is to develop accurate stage–discharge rating curves with limited measurements. The Lake Tana basin in the upper reaches of the Blue Nile in the Ethiopian Highlands is typical for the lack of reliable streamflow data in Africa. On average, one stage–discharge measurement per year is available for the 21 gaging stations over 60 years or less. To obtain accurate and unique stage–discharge curves, the discharge was expressed as a function of the water level and a time-dependent offset from zero. The offset was expressed as polynomial functions of time (up to order 4). The rating curve constants and the coefficients for the polynomial were found by minimizing the errors between observed and predicted fluxes for the available stage–discharge data. It resulted in unique rating curves with R2 > 0.85 for the four main rivers. One of the river bottoms of the alluvial channels increased in height by up to 3 m in 60 years. In the upland channels, most offsets changed by less than 50 cm. The unique rating curves that account for temporal riverbed changes can aid civil engineers in the design of reservoirs, water managers in improving reservoir management, programmers in calibration and validation of hydrology models and scientists in ecological research.

A novel workflow based on physics-informed machine learning to determine the permeability profile of fractured coal seams using downhole geophysical logs

Permeability is arguably the most important parameter in gas pre-drainage and production practices in coal mining and coal seam gas industries. In fractured coal seams, permeability can significantly vary both laterally and vertically. Knowing the vertical variation of permeability in relatively thick coal seams is important in optimizing inseam pre-drainage design in underground coal mining and improving reservoir management and completion design efficiency in the coal seam gas industry. Existing empirical/statistical coal permeability models often yield poor results in estimating permeability profiles where there is significant variation in the characteristics of coal fracture systems. This study presents a novel workflow for evaluating the permeability profile of naturally fractured thick coal seams using downhole geophysical logging data. The workflow combines physics-based simulation, laboratory experiments, and a data-driven machine learning approach for estimating the permeability profile. As part of this workflow, several coal specimens from the study coal seam are first tested under different stresses to measure their permeability, density, and ultrasonic responses. Numerical simulation of the Navier-Stokes fluid flow and elastic wave propagation was then performed on a range of constructed coal block geometries with various fracture densities. In the next step, a physics-informed, neural network-based model (PINN) was trained using the data obtained from both laboratory experiments and numerical simulation. The model inputs included density, as well as compressional and shear wave velocities of the coal seam and its output is the trend of permeability variation across the coal seam interval. In-situ permeability measurement at a certain depth (e.g., from well pressure testing) was then used to convert the permeability trend to the entire permeability profile of the coal seam along its interval. The performance of the proposed workflow was finally evaluated using a suite of downhole geophysical logging data from a borehole intersecting two coal seams in an eastern Australian basin. This study demonstrates that the proposed workflow is relatively simple to implement and yet accurate for evaluating the permeability profile of a coal seam across its interval.

Temperature-responsive smart tracers for field-measurement of inter-well thermal evolution: Heterogeneous kinetics and field demonstration

Temperature-responsive smart tracers enable advanced warning of a “premature thermal breakthrough” and may therefore improve reservoir management and reduce financial risk. A successful calculation of inter-well fluid temperatures requires that temperature-dependent kinetics are known, which typically result from homogeneous batch reactor experiments. However, recent meso-scale field experiments at the Altona Field Laboratory involving fluid flow in a discretely-fractured reservoir suggest that silica-fluid interfaces may accelerate hydrolysis kinetics. Here, the Arrhenius parameters of phenyl acetate hydrolysis are investigated under heterogeneous reaction conditions in packed-bed column experiments. The breakthrough curve of the reaction product is compared to an inert reference tracer (carbon-cored nanoparticles). Temperatures experienced during field testing ranging from 10 to 40 ℃ were studied using a phosphate buffer solution to maintain a near-neutral pH, as found in the reservoir. The empirically-determined pre-exponential factor and activation energy values are subsequently used in a first-order kinetic model to improve calculations of effective reservoir temperatures for these meso-scale field tests. The results suggest that hydrolysis reactions can directly monitor inter-well reservoir temperatures, but only if the influence of solid-fluid interactions are carefully accounted for.

Generating pseudo well logs for a part of the upper Bakken using recurrent neural networks

To develop a reservoir, we need to understand the distribution of key reservoir properties. Those formation properties are mostly derived from well log data. However, obtaining well log data is so expensive that the data we acquire is never sufficient. Therefore, we need to create pseudo data from real measured data. One approach is to generate synthetic well logs using recurrent neural networks (RNNs). Though relatively nascent, it is proven that RNNs allow for the generation of well logs with reasonable accuracy at a fraction of the price. Currently, gamma ray is the most cultivated well log in the E&P process today (owing to its decisive role during drilling operations and affordable price). All the same, a more extensive log suite is required to comprehend the producibility and frackability of hydrocarbon reservoirs like shale reservoirs.In this study, an upper Bakken database of 97 wells which contain gamma ray, bulk density, photoelectric index, density porosity, neutron porosity, true resistivity, compressional and shear slowness was used to develop deep learning models apt to creating synthetic logs for another set of 40 wells in the upper Bakken. The latter wells only had measured gamma ray. Using petroleum engineering concepts and RNNs, data-driven models capable of generating the remaining logs were created. The data-driven models were validated using wells with logs that have been removed from the database to serve as blind validation wells. The performance of the models (5–10 Mean Average Percentage Error and R-squared of 0.6–0.8) suggests the workflow described in this study is a viable way to maximize the value of a gamma ray log. With the ability to generate logs of similar quality for all wells in a shale asset (starting only with gamma ray), superior reservoir evaluation and thus improved reservoir management can be achieved.

An investigation into the impact of reservoir management Kerala floods 2018: A case study of the Kakki reservoir

The coastal state of Kerala, India experienced unprecedented levels of rainfall and flooding in August 2018, resulting in huge life and property loss. Since then the impact reservoir management may have had on the severity of the 2018 Kerala floods has been in question. This study presents a novel approach to developing a reservoir model using HEC-HMS and HEC-ResSim models, combined with satellite remote sensing data. In order to establish a link between flood severity and reservoir management, a model of the Kakki reservoir in southern Kerala was created. Simulations were carried out for six long term, two short term, and two immediate run cases. It was found that all cases except the immediate simulation run resulted in a reduced peak flow. The long simulation run, which altered the guide curve after the heavy rainfall occurring on 14th August 2018, while constraining the outflow, was found to produce the greatest reduction in peak outflow. The significant peak outflow reduction achieved suggests that improved reservoir management could have reduced the severity of the 2018 floods.

Intelligent Completion Installations Instrumental in Brazilian Presalt Development

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 195935, “Hundredth Intelligent Completion Installation: A Milestone in Brazilian Presalt Development,” by Eduardo Schnitzler, SPE, Luciano Ferreira Gonçalez, and Roger Savoldi Roman, Petrobras, et al., prepared for the 2019 SPE Annual Technical Conference and Exhibition, Calgary, 30 September–2 October. The paper has not been peer reviewed. Presalt heterogeneous carbonate reservoirs typically present long net pays, high production and injection rates, and flow-assurance risks. The complete paper presents a discussion of the use of intelligent well completion (IWC) in Santos Basin Presalt Cluster (SBPSC) wells. With the use of this technique and the introduction of several improvements, well-completion time was reduced to less than 50% of that seen in initial wells. Introduction The SBPSC is in ultradeep water—be-tween 1900 and 2400 m—approximately 290 km offshore Rio de Janeiro. From the opening phases of SBPSC development, IWC has been crucial, not only to improve reservoir management but also to provide better capability to deal with reservoir uncertainty. Extensive use of IWC has presented challenges for well-engineering teams because of the complexity associated with this completion design. Minimizing well-construction costs and risks is essential for any project, but, in ultradeep water, it is even more critical. Intelligent Completion Systems Internal risk assessments were conducted to determine optimal solutions for the first production-development project. Direct hydraulic systems were chosen to actuate interval control valves (ICVs) remotely in two- and three-zone wells. This field-proven system was considered the simplest and most reliable for long-term use. However, because the wet tree (WT) designed for SBPSC projects comprises multiplex control systems, addressing these additional integration issues during the planning phase demands exceptional efforts. An extensive integration process had to be conducted to assure that every interface would perform correctly during the production phase. Most projects consider the use of a 95/8- or 97/8-in. production casing set and cemented in front of the reservoir, allowing the use of wider completion equipment, including IWC components. The use of larger-diameter accessories provides the production string the capability to deal with higher mechanical loads, a feature important in the absence of an expansion joint. The full completion string, installed in a single run, has no separated lower completion system. This integral completion design avoids the use of down-hole connections and allows control lines to be run continuously during deployment and the equipment functionality to be tested fully during installation. Dedicated pressure and temperature sensors are located on each interval and inside the completion string. For two-zone production wells, three chemical-injection points are available. Two of them are upstream of the ICVs to prevent scaling formation in the valves by injecting scale inhibitors in the annulus. These chemical-injection points are especially important when producing the well with any of the ICVs in choked positions, leading to higher-pressure drops across the valves. A third chemical-injection system, injecting into the production string, is above the upper packer and may be used for a second chemical injection or as a backup for the main chemical-injection systems.

Efficient workflow for optimizing intelligent well completion using production parameters in real-time

Intelligent Well Completion (IWC) has been successfully deployed over the years to improve reservoir management, with better results in heterogeneous reservoirs. This work proposes an efficient workflow to identify well candidates for Inflow Control Valves (ICV) application and production optimization using parameters in real time. The methodology searches for potential reservoir layer grouping in the producer well, in order to control zonal flow, without expending too much computational time in valve positioning. ICV control strategy uses real-time production guide rates generated by the simulator, reducing optimization parameters. The proposed workflow is applied to a synthetic reservoir model, with properties similar to the Brazilian pre-salt area. This novelty strategy for ICV modelling and control presented a significant reduction in optimization parameters. Results show that Net Present Value (NPV) – and IWC economic gain – are highly dependent on the economic scenario. Nevertheless, the methodology has potential for application in more complex simulations, with greater number of wells or optimization parameters, like multi-position or continuously variable position ICV.

Improving the conceptual understanding of the Darajat Geothermal Field

The Darajat Geothermal Field is the largest vapor-dominated geothermal field in Indonesia with an installed power generation capacity of 271 MWe. For the past 23 years, it has averaged about 90% net capacity factor since commercial operations started in 1994. From the initial 55 MWe, new increments of generation were added in 2000 (95 MWe) and in 2007 (110 MWe; later increased to 121 MWe in 2009). The drilling of wells to support these new increments of generation and continuous resource monitoring has provided key data for integration into the conceptual model and resource simulation modelling.Recent reservoir characterization studies enabled the delineation of surface structures that impact the geothermal system, correlation of surface and subsurface rocks, determination of the orientation of fractures that correlate with feed zones encountered by wells, and better imaging of the reservoir through use of advanced geophysical interpretation methods. The above analyses have helped better define the conceptual model of the Darajat Field and provided information in targeting future make-up wells.Star Energy Geothermal Darajat II (SEGD) takes a proactive approach to manage anticipated and emerging resource performance issues. A good example of this approach is provided by adjustments to the injection of steam condensate from the power plants. Injection initially utilized low productivity wells in the central portion of the field. Field monitoring through geochemical, downhole, and wellhead pressure-temperature (“PT”) measurements indicated that this injection strategy had the potential to adversely affect production from nearby wells by suppressing aquifer boiling and lowering reservoir pressure. Consequently, in 2012, condensate injection was shifted to a new location near the Northeast margin of the field to utilize a well with deep entries. Based on experience and supported with case histories from other vapor-dominated fields, primarily The Geysers, deep injection beneath the feed zones of the production wells is effective at minimizing the impacts of injection breakthrough. Following the change in injection strategy, the central portion of the field has experienced renewed boiling, while only one production well is exhibiting early symptoms of chemical breakthrough from injection in the Northeast.Going forward, expected reservoir management challenges include requirements to add new well pads and target production wells into undrilled areas, localized increased influx of external fluids as the reservoir pressure drops, and reservoir dry-out leading to high superheat levels and reduced well productivity. By continuing to leverage lessons learned to further improve reservoir management processes along with the proactive response to field management issues, SEGD anticipates that Darajat can maintain its production at current levels for the forseeable future.

Intelligent Completion in Water-Injector Well Improves Field Development

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 192850, “Improving Field Development Through Successful Installation of Intelligent Completion in a Water-Injection Well,” by Eglier Yanez, Mattheus Uijttenhout, Maher Zidan, Rail Salimov, Salem Al-Jaberi, Al Anoud Al-Shamsi, Amnah Al-Sereidi, Mohamed Mostafa Amer, Yousef Al-Hammadi, Abdullah Abdul-Halim, Giovani Caletti, Mustapha Adli, Yousif Hasan Al-Hammadi, and Fahad Mustafa Al-Hosani, ADNOC, prepared for the 2018 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 12–15 November. The paper has not been peer reviewed. The operator piloted a new well-completion design combining inflow-control valves (ICVs) in the shallow reservoir and inflow-control devices (ICDs) in the deeper reservoir, both deployed in a water-injector well for the first time in the company’s experience. The objectives were to improve reservoir management, reduce well-construction complexity, and achieve cost optimization. This paper covers the overall aspects of well performance in commingled injection mode. Introduction The offshore field was discovered in 1971 in the Arabian Gulf. On the basis of results from exploratory wells, three main reservoirs were proposed for further development (A, B, and C). The commercial phase of the field started successfully, with first oil in 2015 provided by the two first wellhead towers as part of the Phase I development plan. The completion design described in this paper is aligned with an optimized field-development plan (FDP) resulting from a study initiated in 2017. The main objectives of the study were integrating new acquired data to upgrade static and dynamic reservoir models to identify any opportunity for extra production and cost optimization. Early water breakthrough caused by the initial elected well pattern and the associated oil-producer-to-water-injector spacing is a significant risk. Therefore, accessibility and selectivity to control inflow are strongly recommended for the upper reservoir (A) to mitigate the breakthrough risk and secure long well life and maximize reserves. The initial dual completion, with two horizontal laterals targeting Reservoirs A and B, and a nonaccessible upper drain was removed, and a single controlled completion was placed. Field-Development Plan Pattern. The FDP consisted of two phases. Phase 1 consisted of an early production scheme from two wellhead towers and export of untreated crude to nearby facilities. This phase contained an intensive data-gathering program to reduce reservoir uncertainties and identify future risks and opportunities clearly. Phase 2 is the full field development, containing the bulk of the oil producers and water-injector (WI) wells drilled from new wellhead towers and newly installed offshore treatment facilities. The overall reservoir performance of Reservoir A was significantly better compared with the original basis of assumption. Consequently, the original line-drive development scheme between oil producers and WIs had to be revised in order to avoid early water breakthrough. As a response, the injector wells were placed outside the oil pool. The behavior of Reservoir B met expectations; for that reason, the line-drive well pattern was maintained in this reservoir.

Distribution patterns of petroleum indices based on multifractal and spatial PCA

Currently, the challenges of oil/gas target prospecting by surface soil geochemical exploration are the covers of the prospecting regions. And the arid, sandy, undeveloped deserts of Xinjiang, China, are merely the geographic challenges facing today's oil and gas exploration and production industry. While there is too often much complex information to assimilate and understand for quick and accurate decisions leading to improved reservoir management, advanced technology, such as the development of newer algorithms and numerical methods that can be helpful in exploration to process large and/or old geochemical datasets to improve targeting of buried oil/gas geochemical systems. Herein, we investigated the possibility that geochemical concentration distribution in space may be members of a special class of complex processes, termed as multifractal, which require a large number of exponents to characterize their scaling properties. In addition, the results present that the 15 petroleum indices of study could be classified into three groups according to their multifractal spectra. The three groups of the indices are discriminated well by several multifractal parameters. Such discrimination results are in agreement well with the analytical results by statistical cluster analysis results and principal component analysis method (PCA) included in the software package of GeoDAS. Based on the PCA results, C-A (concentration-area) fractal method has been applied in this paper to delineate the geochemical anomaly areas for petroleum resources prediction. The first and second components clearly represent the potential petroleum prospecting areas which correspond well to the discovered oil/petroleum sites. Both the multifractal method and spatial analysis technique in this paper provide new insights into the selection of target indices of petroleum geochemical exploration as well as comprehensive information extraction for oil/gas target prospecting by surface soil geochemical exploration.

A Regularized Production-Optimization Method for Improved Reservoir Management

Summary Model-based production optimization has become a promising technique in recent years for improving reservoir management and asset development in the petroleum industry. A variety of methods have been developed to address production-optimization problems. However, many solutions resulting from these methods are not ready to be accepted by (operations) engineers because they are difficult to understand and implement in practice. A typical example is the erratically oscillatory, bang-bang-type solution of well-control optimization problems. For this challenge, a regularized optimization problem is formulated in this work with two purposes: to create smooth solutions and to improve the convergence speed. Furthermore, the original, high-dimensional optimization problems can be reduced to low-dimensional ones by a principal-component-analysis (PCA) -based regularization method such that some population-based methods, including genetic algorithm (GA) and particle-swarm optimization (PSO), can be used as search engines to find optimal or improved solutions that tend to have less chance of trapping in local optima. Examples show that the methodology proposed can ensure a continuous transition over time between variables (e.g., well controls) such that the generated solution is more acceptable to the (operations) engineers. Moreover, it significantly speeds up the convergence of the optimization process, allowing large-scale problems to be addressed efficiently.

Effect of hydrocarbon production on reflection amplitude properties of reservoirs—a case study of Kov Field, Niger Delta, Nigeria

A better understanding of how hydrocarbons and other fluids such as brine move in the reservoirs within the Kov Field will enable the improved application of secondary recovery methods such as water and gas injections and also highlight areas within the field that have remaining hydrocarbons. This work focuses on the two major sandstone reservoir intervals, the AG-I and the AG-II all within the Agbada Formation, since they showed prominent amplitude changes. In the AG-I Formation, the results obtained from the production data and rock physics modeling show an increase in the amplitude impedance (AI) contrast around injection wells and a decrease in AI contrast around production wells due to fluid replacement, and it also deals with the pressure changes in correlation to the water injection performed to maintain the reservoir pressure, while in the AG-II Formation, the effect of hydrocarbon production serves to visualize and map the oil-water contact (OWC) as it evolves with the production time; the OWC is not observable on later seismic vintages because the reservoir drops below the OWC. Conclusively, the effect of hydrocarbon production throughout the reservoir and across the entire Field would be beneficial in mapping fluid changes in the Kov Field, which can be utilized for enhanced oil recovery and improved reservoir management.

4D seismic — Past, present, and future

Arco demonstrated time-lapse 3D seismic, or “4D,” in the 1980s. The technology is focused on drilling better wells and improving reservoir management, thus increasing the poor 30–35% average reservoir recovery factor. It is used to extend the peak-flow plateau of the reservoir, deliver low-cost reserves additions, and make better use of topsides facilities. 4D is one of the few technologies that can show what is happening between wells. Its take-up was accelerated by the tendency of reservoir flow rates to come off-peak quickly and unexpectedly, requiring urgent improvements in the understanding of the reservoir. 4D technology led to improved well success rates and produced net-present-value (NPV) figures that were easily eight to 10 times the cost of the 4D seismic itself. It became commonplace to re-record 4D surveys at intervals of between two and five years. The technology has improved markedly since its early use, with nonrepeatability metrics falling from 40% or more down to single figures. It is most effective if conducted early in the reservoir's life and, subsequently, prior to reservoir management changes such as commencement of injection. On large reservoirs, 4D is generally routine, although it is also used on small reservoirs with 30 million bbl remaining reserves or less. Emplaced systems with frequent reshoots have been implemented on some reservoirs. Challenges include 4D on land, hard rock reservoirs, pressure response detection, and incorporation of geomechanical models. The 2015–2017 market downturn has slowed the extension of emplaced systems and other “broadband” techniques for 4D, but new technologies will move into this market, at reduced cost, so the future is full of opportunities.