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In the complete paper, the authors present a novel methodology to model interwell connectivity in mature waterfloods and achieve an improved reservoir-energy distribution and sweep pattern to maximize production performance by adjusting injection and production strategy on the well-control level. A Drilling Advisory System (DAS) is a rig-based drilling-surveillance and -optimization platform that encourages regular drilloff tests, carefully monitors drilling performance, and provides recommendations for controllable drilling parameters to help improve the overall drilling process. This paper proposes a framework based on proxies and rejection sampling (filtering) to perform multiple history-matching runs with a manageable number of reservoir simulations.
This paper presents the performance results from one of the waterflood pilots in the Viewfield Bakken. An 18-well numerical-simulation model was built to represent an operator’s Lower Shaunavon waterflood-pilot area. Numerical simulation was used, and a history match on the pilot area was performed.
The difficulty in applying traditional reservoir-simulation and -modeling techniques for unconventional-reservoir forecasting is often related to the systematic time variations in production-decline rates. This paper proposes a nonparametric statistical approach to resolve this difficulty. In this work, the authors perform automatic decline analysis on Marcellus Shale gas wells and predict ultimate recovery for each well.
Monte Carlo simulation is a process of running a model numerous times with a random selection from the input distributions for each variable. The results of these numerous scenarios can give you a "most likely" case, along with a statistical distribution to understand the risk or uncertainty involved. Computer programs make it easy to run thousands of random samplings quickly. Monte Carlo simulation begins with a model, often built in a spreadsheet, having input distributions and output functions of the inputs. The following description is drawn largely from Murtha.
The growing popularity of model-based optimization work flows has resulted in an increase in their application to field cases. This paper presents an unbiased stochastic data-driven work flow in which surface and subsurface uncertainties are accounted for and their effects on facilities design and operational decisions are quantified. Three-dimensional reservoir models are best created with a combination of well logs and 3D-seismic data. However, the effective integration of these results is not easy because of limited seismic resolution.
The industry increasingly relies on forecasts from reservoir models for reservoir management and decision making. However, because forecasts from reservoir models carry large uncertainties, calibrating them as soon as data come in is crucial. The complete paper explores the use of multilevel derivative-free optimization for history matching, with model properties described using principal component analysis (PCA) -based parameterization techniques. The results of the authors’ research showed promising benefits from the use of a systematic procedure of model diagnostics, model improvement, and model-error quantification during data assimilations. A challenging problem of automated history-matching work flows is ensuring that, after applying updates to previous models, the resulting history-matched models remain consistent geologically.
Unconventional Risk and Uncertainty: What Does Success Look Like? This paper presents approaches for proper risking of uncertain recoverable volumes for an unconventional resource, taking into account the chance of false positives from appraisal-well information. The difficulty in applying traditional reservoir-simulation and -modeling techniques for unconventional-reservoir forecasting is often related to the systematic time variations in production-decline rates. This paper proposes a nonparametric statistical approach to resolve this difficulty. As the drilling industry improves its efforts to capture drilling operation activities in real time, it has generated a significant amount of data that drilling engineers cannot process on their own.
The complete paper discusses a study in which the authors propose a joint field-development and well-control-optimization work flow using high-performance parallel simulation and commercial cloud computing. In upstream oil and gas, cloud computing is very immature because the industry has always been challenged by storage and computational capability. However, high-performance cloud computing may create an opportunity for smaller companies lacking infrastructure for scientific applications.
In the complete paper, the authors reduce nonuniqueness and ensure physically feasible results in multiwell deconvolution by incorporating constraints and knowledge to methodology already established in the literature. In the complete paper, a novel hybrid approach is presented in which a physics-based nonlocal modeling framework is coupled with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. In the complete paper, the authors revisit fundamental concepts of reservoir simulation in unconventional reservoirs and summarize several examples that form part of an archive of lessons learned. Fit-for-purpose tactics likely will be of ever-increasing focus going forward. If it is not adding value, it should not be done.
SPE, through its Energy4me programme, will present a free one-day energy education workshop for science teachers (grades 8–12). A variety of free instructional materials will be available to take back to the classroom. Educators will receive comprehensive, objective information about the scientific concepts of energy and its importance while discovering the world of oil and natural gas exploration and production. Energy4me is an energy educational public outreach programme that highlights how energy works in our everyday lives and promote information about career opportunities in petroleum engineering and the upstream professions. SPE’s Energy4me programme values the role teachers and energy professionals play in educating young people about the importance of energy.