Advanced Data-Driven Performance Analysis For Mature Waterfloods

Mijnarends, Runo; Frolov, Andrey; Grishko, Fedor; Kryanev, Svyatoslav; Mikhaylenko, Egor; Nizamutdinov, Eduard; Plokhotnichenko, Oleg; Irina, Surovets; Ulyanov, Egor; Volokitin, Yakov; Gladkov, Andrey; Belyanushkina, Maria; Lvov, Anton

doi:10.2118/174872-ms

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…In recent years, the concept pair has also found traction within the industry. Mijnarends et al (2015) e.g. illustrated its use in estimating various flow rates and designing useful maps for the oil industry, as well as for designing comprehensive GIS to manage such work.…”

Section: The Originsmentioning

confidence: 99%

Distinguishing extensive and intensive properties for meaningful geocomputation and mapping

Scheider

Huisjes

2018

International Journal of Geographical Information Science

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A most fundamental and far-reaching trait of geographic information is the distinction between extensive and intensive properties. In common understanding, originating in Physics and Chemistry, extensive properties increase with the size of their supporting objects, while intensive properties are independent of this size. It has long been recognized that the decision whether analytical and cartographic measures can be meaningfully applied depends on whether an attribute is considered intensive or extensive. For example, the choice of a map type as well as the application of basic geocomputational operations, such as spatial intersections, aggregations or algebraic operations such as sums and weighted averages, strongly depend on this semantic distinction. So far, however, the distinction can only be drawn in the head of an analyst. We still lack practical ways of automation for composing GIS workflows and to scale up mapping and geocomputation over many data sources, e.g. in statistical portals. In this article, we test a machine-learning model that is capable of labeling extensive/ intensive region attributes with high accuracy based on simple characteristics extractable from geodata files. Furthermore, we propose an ontology pattern that captures central applicability constraints for automating data conversion and mapping using Semantic Web technology.

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Section: The Originsmentioning

confidence: 99%

Distinguishing extensive and intensive properties for meaningful geocomputation and mapping

Scheider

Huisjes

2018

International Journal of Geographical Information Science

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“…To counter further oil production decline and sustain the production at the current level SPD has developed a long-term strategy that includes optimization of ongoing waterflooding (Mijnarends et al, 2015) and implementation of modern EOR techniques such as ASP flooding and LSF . All fields are mature waterfloods with volume-averaged watercut above 80%.…”

Section: Introductionmentioning

confidence: 99%

Low Salinity Flooding Trial at West Salym Field

Erke¹,

Volokitin²,

Edelman³

et al. 2016

SPE Improved Oil Recovery Conference

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Low-salinity waterflooding (LSF) has been recognized as an IOR/EOR technique for both green and brown fields in which the salinity of the injected water is lowered for particular reservoir properties to improve oil recovery. While providing lower or similar UTC's low salinity projects have the advantage of lower capital and operational costs as compared to some more expensive EOR alternatives.This work describes LSF experiments, field-scale simulation results, and conceptual design of surface facilities for West Salym oil field. The field is located in West Siberia and is on stream since 2004. Conventional waterflooding was started in 2005 and current water cut is currently above 80% in the developed area of the field. To counter oil production decline a tertiary Alkaline-Surfactant-Polymer (ASP) flooding technique selected for mature waterflooded field parts and piloting of this technique is ongoing. Operationally simpler and more cost-effective LSF method is considered for implementation in the unflushed (green) areas of the field since it has been recognized that application of LSF in secondary mode results in better incremental oil recovery than LSF in tertiary mode.The results of a comprehensive conceptual study performed to justify the LSF trial are presented in this paper. To generate production forecast for LSF in the isolated area at the outset of reservoir development the results of laboratory core tests executed at different salinities presented earlier have been used. Dynamic reservoir modelling using low-salinity relative permeability curves showed that injection of low-salinity water leads to incremental oil production up to 2.5% of STOIIP. These results establish the fundamentals for a LSF field trial. A concept of surface facilities design for LSF trial area at West Salym oil field is also presented in the paper. Differently to other LSF projects it is proposed to prepare low-salinity water with required properties by mixing fresh water from aquifer and high salinity water from produced water reinjection (PWRI) system. In such a case LSF facilities concept does not require expensive water treatment techniques which significantly reduces the project capital and operational costs.

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Intelligent Tool To Design Drilling, Spacer, Cement Slurry, and Fracturing Fluids by Use of Machine-Learning Algorithms

Shadravan

Tarrahi

Amani

2017

SPE Drilling &Amp; Completion

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Summary Design of drilling fluids, spacers, cement slurries, and fracturing fluids is often done by trial and error in the laboratory. In the first step, the required properties of these fluids are categorized and then efforts will be started with a rough idea of the optimal composition. This first guess usually depends on the experience of the laboratory analyst or fluid engineer. Afterward, the trial-and-error testing starts, and it continues until the fluid design moves closer to the desired fluid criteria. There are several test data that would not be used in this method, and it is difficult to digest a large amount of information by the user. Trial and error could be time-consuming, very costly, and misleading. Today, there is a need for an intelligent system that uses all the available data (big data), even if the data sets are not close to the desired goal, and offers insights for fluid designs. This paper conducted a study on the application of machine-learning-based methodologies, including Gaussian-process regression (GPR) and artificial neural networks (ANNs), to reduce the costs of testing, integrate available experimental data, and eliminate the need for personnel supervision. These practical nonlinear-regression methods empower efficient and fast prediction tools that do not require including complex physics of the underlying system while integrating all available data from different sources. GPR, which is also known as Kriging in geostatistics literature, has exceptional advantages over traditional regression methods because it does not require a known form for regression function and also has the capability of determining the estimation error and the confidence interval. This machine-learning-based tool offers insights for intelligent fluid design and could reduce costs.

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Advanced Data-Driven Performance Analysis For Mature Waterfloods

Cited by 6 publications

References 14 publications

Distinguishing extensive and intensive properties for meaningful geocomputation and mapping

Distinguishing extensive and intensive properties for meaningful geocomputation and mapping

Low Salinity Flooding Trial at West Salym Field

Intelligent Tool To Design Drilling, Spacer, Cement Slurry, and Fracturing Fluids by Use of Machine-Learning Algorithms

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