New Method for Production Data Analysis to Identify New Opportunities in Mature Fields: Methodology and Application

Mohaghegh, Shahab D.; Gaskari, Razi; Jalali, Jalal

doi:10.2118/98010-ms

Cited by 21 publications

(9 citation statements)

References 5 publications

(4 reference statements)

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“…We believe that ITSPM is able to bring out the hidden information about the reservoir characteristics and its behavior, which is embedded within the production and field data. This technology has been incorporated in the top-down, intelligent-reservoir-modeling workflow that has been referenced extensively in this manuscript (Mohaghegh et al 2005;Mohaghegh 2009;Gomez et al 2009;Mohaghegh et al 2011). Geostatistic methods provide a full-field perception of the geological characteristics used in developing the fieldwide model.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…More discussion on these methods is provided in the next section. In contrast to the traditional production-data-analysis methods, datadriven models have been introduced and studied in recent years (Mohaghegh et al 2005;Mohaghegh 2009; Gomez et al 2009). One of the advantages of these methods is their ability to perform the analysis with a limited amount of data (Mohaghegh 2009;Gomez et al 2009).…”

Section: Introductionmentioning

confidence: 99%

“…The main limitations that come with production-data-analysis methods relate to the subjectivity of the process. Also, as mentioned previously, most of these analyses are based on single-well studies, failing to provide a full-field, cohesive model that can be used for production-prediction purposes (Mohaghegh et al 2005). Systematic production-data analysis begins in the techniques presented by Arps (1945).…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Production Modeling Using Full Field Pattern Recognition

Khazaeni

Mohaghegh

2011

SPE Reservoir Evaluation &Amp; Engineering

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Production-data analysis has been applied extensively to predicting future production performance and field recovery. These applications operate mostly on a single-well basis. This paper presents a new approach to production-data analysis using artificial-intelligence (AI) techniques in which production history is used to build a fieldwide performance-prediction model. In this work, AI and data-driven modeling are used to predict future production of both synthetic-(for validation purposes) and real-field cases.In the approach presented in this article, production history is paired with field geological information to build data sets containing the spatio-temporal dependencies among different wells. These dependencies are addressed by compiling information from closest offset wells (COWs) that includes their geological and reservoir characteristics (spatial data) as well as their production history (temporal data).Once the data set is assembled, a series of neural networks are trained using a back-propagation algorithm. These networks are then fused together to form the intelligent time-successive production-modeling (ITSPM) system. This technique uses only the widely available measured data such as well logs and production history of existing wells to predict future performance of the existing wells and production performance of the new (infill) wells. To demonstrate the applicability of this method, a synthetic oil reservoir is modeled using a commercial simulator. Production and well-log data are extracted into an all-inclusive data set. Several neural networks are trained and validated to predict different stages of the production. The ITSPM method is used to estimate the production profile for nine new wells in the reservoir. Furthermore, ITSPM is also applied to two giant oil fields in the Middle East. The first one has more than 200 wells and 40 years of production history. ITSPM's production predictions of the four newest wells in this reservoir are compared with their actual production. The second real field has hundreds of wells producing from multiple layers. The field has undergone waterflooding for almost its whole life. This case also shows the capabilities of this technique in more-complex scenarios and especially multiphase systems. Production-Data-Analysis BackgroundMature fields that have been producing for a long time do not provide much reservoir-characterization data. This is because of their age and the lack of historical data from their old wells. In the time that oil price might not be in its highest range, infill-drilling decisions should be made with a better degree of understanding from such fields in order to have an economically better decision.Using production data as a tool in decision making has been an interest of petroleum engineers for many years. Different methods

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intelligent Production Modeling Using Full Field Pattern Recognition

Khazaeni

Mohaghegh

2011

SPE Reservoir Evaluation &Amp; Engineering

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“…Fuzzy logic helps to guarantee precision and quality and to avoid inconsistency and uncertainty (Bilgen, 2010). Fuzzy logic applications in E&P operations have been reported in different areas such as engineering design and control (Nikravesh et al 1997;Sengul and Bekkousha 2002;Mohaghegh et al 2005;Widarsono et al 2005;Cao et al 2006;Taheri 2008;Weiss et al 2001;Zarei et.al. 2008;Murillo et al 2009) and also in operations of production facilities, systems and optimal well operations (Rivera and Farabee 1994;Dumans 1995;Xiong et al 2001;Alimonti and Falcone 2004;Garrouch and Lababidi 2005)…”

Section: Application Of Fuzzy Logic In Eandpmentioning

confidence: 99%

A review of fuzzy logic applications in petroleum exploration, production and distribution operations

Okwu

Nwachukwu

2018

J Petrol Explor Prod Technol

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Petroleum exploration and production business thrives with in-depth knowledge and understanding of the subsurface. Technological advancement has helped in furnishing the industry with much information about the petroleum reservoir; however, a lot of uncertainties still exist due to the nature of the subsurface. The industry has strived to address this problem in diverse ways; regrettably, the classical methods relied upon have failed to provide a proper guide to management decision in exploiting these reservoirs. In recent times, artificial intelligence techniques, particularly Fuzzy Logic (FL), have been identified as a potential tool to deal with the uncertainties encountered in most exploration and production (E&P) operations. This research provides a review of FL applications in E&P operations under non-deterministic input parameters, possible challenges and solution procedures using FL sensitivity analysis and rule viewers. The focus is on reservoir characterization for reservoir evaluation, drilling/completion operations and stimulation treatment. The study also examines the extent FL could be applied to extract useful information from the large volume of historical oil and gas data already on the shelf and the future gaps to fill. A case study was presented which considered cost optimization in Liquefied Petroleum Gas (LPG) distribution operations using fuzzy logic.

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“…During the screening of production data analysis techniques (8)(9)(10)(11)(12)(13)(14)(15)(16)(17) , which can help derive useful information (interwell connectivity in particular), this method was chosen because of its ability to manage non-stationarity issues. This ability is very important in obtaining meaningful correlations for wells that were started at different dates and have considerable production and complex shut-in history.…”

Section: Wavelet Analysis Of Production Datamentioning

confidence: 99%

Development of Marginal/Mature Oil Fields: A Case Study of the Sinclair Field

Resnyanskiy

Babadagli

2010

Journal of Canadian Petroleum Technology

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Development of marginal/mature fields has become popular because of a significant decline in new field discoveries and high oil prices. In particular, small size fields of this kind are more challenging because of limited options for development. This paper presents a study on the Sinclair field located in Alberta, Canada. The field has 19 wells, six of which are horizontal, and have been in production for more than 20 years. Despite the quality of oil (40°API, 1.5 cp) and rock properties (20% average porosity, water-wet sandstone), the current production is less than 100 bbl/D for the whole field. The field is now undergoing waterflooding. The main challenges are the thin pay zone (~4 m), severe water production and a puzzling recovery factor of approximately 10%. The current study consists of three phases: numerical reservoir modelling and history match to understand the reasons for low oil production and to analyze the hydrodynamic characteristics of the field, characterization of reservoir and interwell connectivity using static and production data and proposing an enhanced oil recovery technique supported by field scale numerical simulation. After modelling and history matching stages, potential reserves locations are estimated for possible dilute surfactant injection. Based on interwell connectivity, different injection schemes that use some producers as injectors are tested. The obtained results are subject to further evaluation and analysis to derive the economic viability of the field.

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New Method for Production Data Analysis to Identify New Opportunities in Mature Fields: Methodology and Application

Cited by 21 publications

References 5 publications

Intelligent Production Modeling Using Full Field Pattern Recognition

Intelligent Production Modeling Using Full Field Pattern Recognition

A review of fuzzy logic applications in petroleum exploration, production and distribution operations

Development of Marginal/Mature Oil Fields: A Case Study of the Sinclair Field

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