This paper describes and evaluates three numerical methods {or the simulation of well coning behavior. The jirst meihoa' empioys the impiicit pressure-explicit saturation (IMPES) analysis with the production terms treated implicitly. The second technique is similar to the /irst model except that the interb[ock transmissibilities are also treated implicitly in the saturation equation. The third model is /ully implicit with respect to all variables in a manner qualified in the Ur, u u CGii@Gii'SOii 0] >trrlulu LtuTL Tesu!is uitb a laboratory coning experiment. Also presented is an analysis of truncation error and a comparison of computational work requirements.
In today's data-driven economy, operators that integrate vast stores of fundamental reservoir and production data with the highperformance predictive analytics solutions can emerge as winners in the contest of maximizing estimated ultimate recovery (EUR). The scope of this study is to demonstrate a new workflow coupling earth sciences with data analytics to operationalize well completion optimization. The workflow aims to build a robust predictive model that allows users to perform sensitivity analysis on completion designs within a few hours. Current workflows for well completion and production optimization in unconventional reservoirs require extensive earth modeling, fracture simulation, and production simulations. With considerable effort and wide scale of sensitivity, studies could enable optimized well completion design parameters such as optimal cluster spacing, optimal proppant loading, optimal well spacing, etc. Yet, today, less than 5% of the wells fractured in North America are designed using advanced simulation due to the required level of data, skillset, and long computing times. Breaking these limitations through parallel fracture and reservoir simulations in the cloud and combining such simulation with data analytics and artificial intelligence algorithms helped in the development of a powerful solution that creates models for fast, yet effective, completion design. The approach was executed on Eagle Ford wells as a case study in 2016. Over 2000 data points were collected with completion sensitivity performed on a multithreaded cluster environment on these wells. Advanced machine learning and data mining algorithms of data analytics such as random forest, gradient boost, linear regression, etc. were applied on the data points to create a proxy model for the fracturing and numerical production simulator. With the gradient boost technique, over 90% accuracy was achieved between the proxy model and the actual results. Hence, the proxy model could predict the wellbore productivity accurately for any given change in completion design. The operators now had a much simpler model, which served as a plug-and-play tool for the completion engineers to evaluate the impact of changes in completion parameters on the future well performance and making fast-tracked economic decisions almost in real time. The approach can be replicated for varying geological and geomechanical properties as operations move from pad to pad. Although the need for heavy computing resource, simulation skillset, and long run times was eliminated with this new approach, regular QA/QC of the model through manual simulations makes the process more robust and reliable. The methodology provides an integrated approach to bridge the traditional reservoir understanding and simulation approach to the new big data approach to create proxies, which allows operators to make quicker decisions for completion optimization. The technique presented in this paper can be extended for other domains of wellsite operations such as well drilling, artificial lift, etc. and help operators evaluate the most economical scenario in close to real time.
Multiwell pads have become a norm in unconventional reservoirs with wells having multistage, multiperforation-cluster configuration. The complex jungle of wells and horizontal laterals under the surface of the earth has raised the complexity of pad optimization and hydraulic fracturing to a next level. Modeling the hydraulic fracture systems correctly is key for optimizing well and stage locations. Even in the case of noncomplex, planar fractures, the interaction amongst the fractures, which is also known as stress shadow effect, can lead to uneven fracture growth. In the case of complex fracture networks, the fracture interaction can be even stronger due to typically higher fracture density. Therefore, stress shadowing is a key element in reservoir modeling for shale plays in the framework of hydraulic fracturing. The sequence of stage placement in the pad and their treatment can also have a significant impact on the reservoir contact.The zipper fracturing technique, involving the simultaneous and back-to-back stimulation of horizontal wells on a pad, has been rapidly adopted by multiple operators in the last few year across various shale plays in North America. The technique has achieved its popularity due to increased efficiency and reduced turnaround time for a multiwell pad. The method has been effective in saving tens of millions of dollars for operators by accelerating the pad development cycle. Apart from improved completion efficiency, pads that have run zipper fracturing have shown improved production from their counterparts in the same field that have been completed without zipper fracturing. The impact on hydraulic fracture growth due to stress shadow from offset wells' hydraulic fracture systems is a major contributing factor for this difference. Depending on the time spent between the stages, the extent and magnitude of stress shadow will change to dictate the growth of the offset well hydraulic fracture stage.Proper reservoir modeling can help optimize the well location and spacing, completion staging, and optimizing hydraulic fracture treatment designs as well as their sequence. This study reviews and discusses the application of stress shadow modeling for various treatment sequences for multiple wells in a pad-and use of numerical reservoir simulation for optimal pad development strategy. The results assess the impact of managing the fracturing sequence and accounting for the delay in time between stages, growth of hydraulic fractures and their interference amongst each other, distribution of proppants and fluids, and reservoir drainage through numerical simulation for unconventional reservoirs.
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