Day 2 Wed, September 22, 2021最新文献

{"title":"A Superior Shale Oil EOR Method for the Permian Basin","authors":"R. Downey, K. Venepalli, J. Erdle, Morgan Whitelock","doi":"10.2118/206186-ms","DOIUrl":"https://doi.org/10.2118/206186-ms","url":null,"abstract":"\u0000 The Permian Basin of west Texas is the largest and most prolific shale oil producing basin in the United States. Oil production from horizontal shale oil wells in the Permian Basin has grown from 5,000 BOPD in February, 2009 to 3.5 Million BOPD as of October, 2020, with 29,000 horizontal shale oil wells in production. The primary target for this horizontal shale oil development is the Wolfcamp shale. Oil production from these wells is characterized by high initial rates and steep declines. A few producers have begun testing EOR processes, specifically natural gas cyclic injection, or \"Huff and Puff\", with little information provided to date. Our objective is to introduce a novel EOR process that can greatly increase the production and recovery of oil from shale oil reservoirs, while reducing the cost per barrel of recovered oil.\u0000 A superior shale oil EOR method is proposed that utilizes a triplex pump to inject a solvent liquid into the shale oil reservoir, and an efficient method to recover the injectant at the surface, for storage and reinjection. The process is designed and integrated during operation using compositional reservoir simulation in order to optimize oil recovery. Compositional simulation modeling of a Wolfcamp D horizontal producing oil well was conducted to obtain a history match on oil, gas, and water production. The matched model was then utilized to evaluate the shale oil EOR method under a variety of operating conditions. The modeling indicates that for this particular well, incremental oil production of 500% over primary EUR may be achieved in the first five years of EOR operation, and more than 700% over primary EUR after 10 years. The method, which is patented, has numerous advantages over cyclic gas injection, such as much greater oil recovery, much better economics/lower cost per barrel, lower risk of interwell communication, use of far less horsepower and fuel, shorter injection time, longer production time, smaller injection volumes, scalability, faster implementation, precludes the need for artificial lift, elimination of the need to buy and sell injectant during each cycle, ability to optimize each cycle by integration with compositional reservoir simulation modeling, and lower emissions.\u0000 This superior shale oil EOR method has been modeled in the five major US shale oil plays, indicating large incremental oil recovery potential. The method is now being field tested to confirm reservoir simulation modeling projections. If implemented early in the life of a shale oil well, its application can slow the production decline rate, recover far more oil earlier and at lower cost, and extend the life of the well by several years, while precluding the need for artificial lift.","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75673792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced Reservoir Simulation: A Novel Robust Modelling of Nanoparticles for Improved Oil Recovery","authors":"L. Hendraningrat, S. Majidaie, Nor Idah Ketchut, F. Skoreyko, Seyed Mousa Mousavimirkalaei","doi":"10.2118/205927-ms","DOIUrl":"https://doi.org/10.2118/205927-ms","url":null,"abstract":"\u0000 The potential of nanoparticles, which are classified as advanced fluid material, have been unlocked for improved oil recovery in recent years such as nanoparticles-assisted waterflood process. However, there is no existing commercial reservoir simulation software that could properly model phase behaviour and transport phenomena of nanoparticles. This paper focuses on the development of a novel robust advanced simulation algorithms for nanoparticles that incorporate all the main mechanisms that have been observed for interpreting and predicting performance.\u0000 The general algorithms were developed by incorporating important physico-chemical interactions that exist across nanoparticles along with the porous media and fluid: phase behaviour and flow characteristic of nanoparticles that includes aggregation, splitting and solid phase deposition. A new reaction stoichiometry was introduced to capture the aggregation process. The new algorithm was also incorporated to describe disproportionate permeability alteration and adsorption of nanoparticles, aqueous phase viscosities effect, interfacial tension reduction, and rock wettability alteration. Then, the model was tested and duly validated using several previously published experimental datasets that involved various types of nanoparticles, different chemical additives, hardness of water, wide range of water salinity and rock permeability and oil viscosity from ambient to reservoir temperature.\u0000 A novel advanced simulation tool has successfully been developed to model advanced fluid material, particularly nanoparticles for improved/enhanced oil recovery. The main scripting of physics and mechanisms of nanoparticle injection are accomplished in the model and have acceptable match with various type of nanoparticles, concentration, initial wettability, solvent, stabilizer, water hardness and temperature. Reasonable matching for all experimental published data were achieved for pressure and production data. Critical parameters have been observed and should be considered as important input for laboratory experimental design. Sensitivity studies have been conducted on critical parameters and reported in the paper as the most sensitive for obtaining the matches of both pressure and production data.\u0000 Observed matching parameters could be used as benchmarks for training and data validation. Prior to using in a 3D field-scale prediction in Malaysian oilfields, upscaling workflows must be established with critical parameters. For instance, some reaction rates at field-scale can be assumed to be instantaneous since the time scale for field-scale models is much larger than these reaction rates in the laboratory.","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75104569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuum-Scale Gas Transport Modeling in Organic Nanoporous Media Based on Pore-Scale Density Distributions","authors":"Zizhong Liu, Hamid Emami‐Meybodi","doi":"10.2118/205886-ms","DOIUrl":"https://doi.org/10.2118/205886-ms","url":null,"abstract":"\u0000 This paper presents a continuum-scale diffusion-based model informed by pore-scale data for gas transport in organic nanoporous media. A mass transfer and adsorption model is developed by considering multiple transport and storage mechanisms, including bulk diffusion and Knudsen diffusion for free phase, surface diffusion for sorbed phase, and multilayer adsorption. The continuum-scale diffusion-based governing equation is developed solely based on free phase concentration for the overall mass conservation of free and sorbed phases, carrying a newly-defined effective diffusion coefficient and a capacity factor to account for multilayer adsorption. Diffusion of free and sorbed phases is coupled through the pore-scale simplified local density method based on the modified Peng-Robinson equation of state for confinement effects. The model is first utilized to analyze pore-scale adsorption data from the krypton (Kr) gas adsorption experiment on graphite. Then we implement the model to conduct sensitivity analysis for the effects of pore size on gas transport for Kr-graphite and methane-coal systems. The model is finally used to study Kr diffusion profiles through a coal matrix obtained through X-ray micro-CT imaging. The results show that the sorbed phase occupies most of the pore space in organic nanoporous media due to multilayer adsorption, and surface diffusion contributes significantly to the total mass flux. Therefore, neglecting the volume of sorbed phase and surface diffusion in organic nanoporous rocks may result in considerable errors. Furthermore, the results reveal that implementing a Langmuir-based model may be erroneous for an organic-rich reservoir with nanopores during the early depletion period when the reservoir pressure is high.","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77610085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated Technologies Ensuring Integrity Throughout the Facility Lifecycle","authors":"A. Dange, G. Varghese, H. Mesbah","doi":"10.2118/206185-ms","DOIUrl":"https://doi.org/10.2118/206185-ms","url":null,"abstract":"\u0000 \u0000 \u0000 Integrity of the wells and facilities is planned right from the reservoir development phase. In the pilot phase all the contributing parameters are collected and considered in the design of the production facilities. As the corrosion/erosion is very important aspect to determine the operating condition and the metallurgy of the facilities/completion, due consideration must be given to the technologies helping the infrastructure planning. However, once the production begins, the real time corrosion monitoring is essential as the reservoir produces from multiple zones along with solids during the complete lifecycle. The sand erosion aggravates the corrosion and can cause leaks around the wellheads and areas with changes in cross section. There are several processes such as inhibitor dosage, chemical treatment are performed from the startup and continued throughout the pilot. The paper covers integrated technologies to minimize the risk of corrosion damages by providing predictive analytics for corrosion and erosion impact. This includes chemical injection system, trace detector, non-intrusive corrosion monitoring, sand detector technologies as a holistic solution and best practice for ensuring asset integrity.\u0000 \u0000 \u0000 \u0000 With the given information on the fluid corrosivity, the corrosion inhibitor and its dosing rate gets identified. Continous injection leads to the formation of a thin film on the entire system which need to be protected. However, many times the dosage is not optimized often leading to over injection or under injection of the chemicals. The injection rate is important to be monitored and optimized with a Realtime corrosion monitoring and gauging the impact on the asset integrity. The non-intrusive easy to install Realtime corrosion monitoring probe can provide real time monitoring for all the above requirements and in remote locations inaccessible during inspection A tracer is added to the chemicals to identify the residual through the tracer meter, which is hooked up with the chemical injection system, to optimize the set dosing rate. The corrosion monitoring system is in a corrosion prone location where the highest corrosion rate is expected to optimize the dosage. The sand detector can be considered in case we are producing from unconsolidated sand reservoir. This helps to identify erosion and where more sand is expected.\u0000 \u0000 \u0000 \u0000 Integrating all these technologies helps optimize the chemical used by around 20% and maximize the lifetime for the integrity by 70%. Also, it predicts potential failures in the system.\u0000 As the data is stored and accessed from different locations, the organization will have a better control on the full integrity which lead to better design and alternating the corrosion inhibitor without any risk on the integrity.\u0000 However, the combined technologies will be high CAPEX, but it will save a lot of OPEX on the long run which is demonstrated in the paper and will provide a good historical data for the field d","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"379 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77880232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Well Interference Detection from Long-Term Pressure Data Using Machine Learning and Multiresolution Analysis","authors":"Dante Orta Alemán, R. Horne","doi":"10.2118/206354-ms","DOIUrl":"https://doi.org/10.2118/206354-ms","url":null,"abstract":"\u0000 Knowledge of reservoir heterogeneity and connectivity is fundamental for reservoir management. Methods such as interference tests or tracers have been developed to obtain that knowledge from dynamic data. However, detecting well connectivity using interference tests requires long periods of time with a stable reservoir pressure and constant flow-rate conditions. Conversely, the long duration and high frequency of well production data have high value for detecting connectivity if noise, abrupt changes in flow-rate and missing data are dealt with. In this work, a methodology to detect interference from longterm pressure and flow-rate data was developed using multiresolution analysis in combination with machine learning algorithms. The methodology presents high accuracy and robustness to noise while requiring little to no data preprocessing.\u0000 The methodology builds on previous work using the Maximal Overlap Wavelet Transform (MODWT) to analyze long-term pressure data. The new approach uses the ability of the MODWT to capture, synthesize and discriminate the relevant reservoir response for each individual well at different time scales while still honoring the relevant flow-physics. By first applying the MODWT to the flow rate history, a machine learning algorithm was used to estimate the pressure response of each well as it would be in isolation. Interference can be detected by comparing the output of the machine learning model with the unprocessed pressure data.\u0000 A set of machine learning, and deep learning algorithms were tested including Kernel Ridge Regression, Lasso Regression and Recurrent Neural Networks. The machine learning models were able to detect interference at different distances even with the presence of high noise and missing data. The results were validated by comparing the machine learning output with the theoretical pressure response of wells in isolation. Additionally, it was proved that applying the MODWT multiresolution analysis to pressure and flow-rate data creates a set of \"virtual wells\" that still follow the diffusion equation and allow for a simplified analysis.\u0000 By using production data, the proposed methodology allows for the detection of interference effects without the need of a stabilized pressure field. This allows for a significant cost reduction and no operational overhead because the detection does not require well shut-ins and it can be done regardless of operation opportunities or project objectives. Additionally, the long-term nature of production data can detect connectivity even at long distances even in the presence of noise and incomplete data.","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81540303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Graph Network Based Approach for Reservoir Modeling","authors":"Wenyue Sun, S. Sankaran","doi":"10.2118/206238-ms","DOIUrl":"https://doi.org/10.2118/206238-ms","url":null,"abstract":"\u0000 Reservoir management routinely requires assimilating historical data and predicting field performance against multiple production strategies before implementing them in the field. However, traditional numerical methods are often cumbersome to characterize, build and calibrate at a timescale that can be used reliably for such short-term decision cycles such as production forecasting, IOR optimization and production rate control. Simpler analytical models make assumptions and lack the rigor needed to adequately model these systems. Pure data-driven methods may lack physical insights or have limited range of applicability. Model fidelity, speed, interpretability, suitability to automate and ease-of-use are some key modeling traits that are desired for reservoir management purposes.\u0000 In this work, we propose to use a reservoir graph-network modeling approach (RGNet), based on the concept of diffusive time of flight, to forecast well performance using routinely measured field measurements (e.g. bottomhole pressure and rates). We propose a novel, model order reduction method based on discretized time of flight for multiple wells with interference. It simplifies the 3D reservoir flow problem into a flow network representation that can be solved as a 2D simulation model with any general-purpose reservoir simulator. Parameters in RGNet model cover well productivity index, grid pore volume and transmissibility, which are estimated through a history-matching process. After history matching, multiple posterior RGNet models are generated to quantify subsurface uncertainties. The RGNet modeling approach allows various fluid-flow physics to be modeled within the grids and boundary conditions, and is applicable to a range of conventional and unconventional reservoirs with different flow mechanisms.\u0000 We applied the proposed approach on a field case reservoir models for multiple wells with interference. By virtue of the reduced complexity, the modeling methodology is highly scalable and still retains physical interpretability. The calibration method produces parsimonious models and provides uncertainty estimates in history matching parameters with range of outcomes. In addition, the RGNet models are much faster to simulate, over 1000x speed up, compared with full-physics models. We then used RGNet models for well-control and flood optimization and achieved significant improvement over field net-present-values.\u0000 Parameterization of the proposed reservoir graph-network modeling approach provides a unique and sustainable way to reduce model complexity needed for reservoir management purposes. The method is rooted in physical principles and provides an explainable dynamic reservoir model that can be effectively used to understand reservoir behavior and optimize performance. The lightweight model lends itself naturally to fast computation that are required for scenario analysis and optimization.","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84323733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Reservoir Fluid and Injection Gas on Shales Huff-N-Puff Performance in the Presence of Diffusion, Sorption, and Hysteresis","authors":"K. Enab, Hamid Emami‐Meybodi","doi":"10.2118/206194-ms","DOIUrl":"https://doi.org/10.2118/206194-ms","url":null,"abstract":"\u0000 We assess the huff-n-puff performance in ultratight reservoirs (shales) by conducting large-scale numerical simulations for a wide range of reservoir fluid types (retrograde condensate, volatile oil, black oil) and different injection gases (CO2, C2H6, C3H8) by considering relative permeability hysteresis, diffusion, and sorption. A dual-porosity naturally fractured numerical compositional model is used that considers molecular diffusion and sorption to represent the flow mechanisms during the injection process. Killough's method, Langmuir's adsorption model, and Sigmund correlation are utilized to incorporate hysteresis, sorption, and diffusion, respectively. To investigate the impact of the fluid type, we consider three fluid types from Eagle Ford shale representing retrograde condensate, volatile oil, and black oil. We conduct a comprehensive evaluation of the impact of diffusion, sorption, and hysteresis on the production performance and retention of each fluid and injection gas. Eagle Ford formation is selected because it is the most actively developed shale, and it contains a wide span of PVT windows from dry gas to black oil.\u0000 The simulation results show that the huff-n-puff process improves the oil recovery by 4-6% when 10% PV of gas is injected. The huff-n-puff efficiency increases with reducing gas-oil-ratio (GOR) as oil recovery from low (GOR) reservoirs is doubled, while recovery from retrograde condensate increased by 20%. C2H6 provides the highest recovery for the black and volatile oil, and CO2 provides the highest recovery for retrograde condensate fluid type. Diffusion and sorption are essential mechanisms to be considered when modeling gas injection to any fluid type in shales. However, the relative permeability hysteresis effect is not significant. Neglecting diffusion during the huff-n-puff process underestimates the oil recovery and retention capacity. The diffusion effect on the oil density reduction is observed more during the soaking period. The diffusion impact increases with higher GOR reservoirs, while the sorption impact decreases with higher GOR. The retention capacity of the injected gas decreases with higher GOR. The diffusion impact on the retention capacity increases with higher GOR. Hence sorption and diffusion must be considered when modeling the huff-n-puff process in ultratight reservoirs.","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84359507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation of Subsea and Platform Production Schemes to Quantify Flow Assurance Risks under Transient and Steady State Conditions in Offshore Kuwait","authors":"E. Al-Safran","doi":"10.2118/206275-ms","DOIUrl":"https://doi.org/10.2118/206275-ms","url":null,"abstract":"\u0000 In offshore production, the type of field development scheme is crucial aspect due to its associated flow assurance risks, which affect project economic, safety, and sustainability. The objective of this study is to simulate and evaluate two offshore field development schemes, namely subsea and platform in offshore Kuwait. Further objective is to carry out detailed transient simulation study on the subsea scheme to investigate flow assurance risks related to terrain slugging, and hydrates formation during shut-in and re-start transient events. The evaluation of the two schemes is based on the associated flow assurance risks, and project economics. Steady state simulations are used to identify the feasible production scheme, which is further simulated under transient shut-in/restart events to investigate flow assurance risks related to terrain slugging and hydrates formation. The steady state simulation results of this study showed that flow assurance risks such as hydrates and pipeline corrosion are significant in both production schemes. To mitigate these risks, sixteen different field development designs of both production schemes were simulated and economically evaluated. Results revealed that the subsea multiphase development scheme with 10-in. ID carbon steel multiphase flowline and 0.3-in. thick polypropylene thermal insulation is the optimum design. Consequently, the optimum design is further analyzed under transient conditions, resulting in appreciable risk of terrain slugging due to hilly-terrain pipeline configuration, especially for the low production rate cases. The transient shut-in/restart simulation results revealed a risk of hydrates formation due to cooling effect during shut-in, which is mitigated by MEG injection. In conclusion, the subsea multiphase flow scheme is selected over platform scheme due to manageable flow assurance risks, low capital investment cost, and minimum environmental impact. This study would enable Kuwait Oil Company to evaluate different offshore development schemes to ensure sustainable production with safe operation and protected environment.","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85098286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modern Look at Uncertainty in Conceptual Geological Modelling. Development of the Decision Support System for Petroleum Exploration","authors":"K. Chirkunov, Anastasiia Gorelova, Z. Filippova, O. Popova, A. Shokhin, Semen Zaitsev","doi":"10.2118/206078-ms","DOIUrl":"https://doi.org/10.2118/206078-ms","url":null,"abstract":"\u0000 At the early stages of field life, the subsurface project team operates under lack of information. Due to the high uncertainties, decisions at the exploration and appraisal stages are often influenced by cognitive distortion that leads to overestimation or underestimation of hydrocarbon reserves and, as a result, to suboptimal investment decisions.\u0000 World practice allows us to identify the most common causes of cognitive bias: the team focus on the most provable according to their view scenario and may ignore data that contradicts the chosen scenario,the opinions of the team members differ in the choice of the most likely scenario,the team members work with geological and geophysical (G&G) data performing separate tasks and may miss important connections between various sources of information.\u0000 The consequences of these cognitive distortions cause an increase in risk capital, the duration of exploration activities, and the choice of suboptimal field developmentstrategy resulting in a decrease in the effectiveness of the exploration program and the project as a whole.\u0000 To reduce such risks, it is possible to attract subject matter experts with extensive experience to support the project team. But the amount of experts is limited and this approach cannot be implemented for the entire portfolio of exploration projects.\u0000 As result of a research project of Gazpromneft in a partnership with IBM Research, an innovative approach was developed for the objective integration of geological and geophysical data. The main idea of this approach is to support the geologist's decisions by an intelligent assistant working on the principles of the modern theory of knowledge engineering. Using the generalized expert knowledge, the intelligent assistant impartially integrates disparate geological information into a set of conceptual geological models (scenarios, objectively evaluates their probabilities, and helps to plan optimal exploration/appraisal activities.","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"118 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84199442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Welcome to the 21st Century for Project Managers","authors":"Analise J Thompson, F. Chaban, Tony Strathman, Dávid Gönczi","doi":"10.2118/205942-ms","DOIUrl":"https://doi.org/10.2118/205942-ms","url":null,"abstract":"\u0000 If the O&G industry adopted new mail technology at the same rate it adopts project management technologies, it would still be using the Pony Express. Risk aversion and resistance to change are two of the main reasons for project failure across the industry. The industry still solves problems by throwing a bunch of human resources at the issue. The more people in the room the better the solution will be, right? In the 21st century, project management needs be based on the interaction of technology with human behavior. The objective of this paper is to introduce the industry to project management in the 21st century.\u0000 In today's ever-changing global economy, the definition of success is just as fluid, and project management must be agile enough to deal with this. Finding something that works and then sticking to it for decades will no longer suffice. Modern technology companies take a unique approach to major project management which continually polls for changes and empowers individual employees to use their own best judgement while maintaining coordination with their fellows. An examination of this approach can provide helpful insight into optimizing the use of available resources, human or otherwise.\u0000 Today's top technologies make it easy for individual team members to continuously update and record the progression of the project, and helps employees work toward better solutions rather than limiting themselves to the original requirements and company protocol. Employees are empowered to look for solutions, think out of the box and outside of what is currently available in-house. In the 21st century, the solution to problems is not a complex spreadsheet shared on SharePoint, it's an elegant integration of technology that optimizes human performance as shown in this case study.","PeriodicalId":10928,"journal":{"name":"Day 2 Wed, September 22, 2021","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78099476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}