Video: Rethinking Appraisal: Identification of Pre- and Post-Sanction Uncertainty Drivers in Deep and Ultra Deep Gulf of Mexico Fields Using Data Mining and Data Analytics

Authors
A. Amirlatifi (Mississippi State University) | J. R. Mills (DeepStar Committee-BP) | O. Abou-Sayed (Advantek International) | G. Block (Advantek International) | A. S. Abou-Sayed (Advantek International) | A. Zidane (Advantek International)
Document ID
OTC-28945-PT
Publisher
Offshore Technology Conference
Publication Date
2018
Document Type
Presentation
Language
English
Copyright
2018. Copyright is retained by the author. This presentation is distributed by SPE with the permission of the author. Contact the author for permission to use material from this video.
Disciplines
7.6.4 Data Mining, 7 Management and Information, 4.3.4 Scale, 7.6 Information Management and Systems, 1.6 Drilling Operations
Keywords
EUR Variance, Field Appraisal, Gulf of Mexico, Variable Importance in Projection, Data Mining and Data Analytics
Downloads
0 in the last 30 days
1 since 2007
Show more detail
View rights & permissions

Appraisal is a key step in consenting to develop an asset, or abandoning it, and is pursued after successful drilling of an exploration well in a potential field. During the appraisal process the drainage area and original hydrocarbons in place, as well as ultimate recovery (EUR) from the field are estimated which are often based on minimum set of information gathered during the exploration phase. This lack of data, along with uncertainties surrounding the appraisal data, introduces high degrees of variations in pre- and post- sanction EURs (EUR). These estimates, however, are revisited each time new data becomes available and as a result, the EUR from a field, along with several other factors, is subject to change over the field lifespan. Identifying the key drivers in accurate pre-sanction estimation of ultimate recovery and reducing post sanction EUR variance, helps in resource allocation and sustainable field development.

A major hurdle faced in subsurface characterization of assets is the degree of dependency between attributes and, the often non-linear behavior of these attributes. One way of overcoming these limitations is regression analysis; however, even in a high accuracy fit, regression coefficients by themselves are not necessarily good measures for ranking attributes, and elimination of lower ranked attributes would result in a new ranking of the remaining attributes. In the present study, several data mining techniques are applied on a dataset of 152 deep and ultra-deep water (D&UDW) fields in the Gulf of Mexico (GoM) to determine which of the 77 well-, reservoir- and field-scale attributes best capture the EUR variance for different fluid types in D&UDW fields in the GoM.

Unlike the conventional regression approaches, the present study offers a robust and stable ranking of attributes with high accuracy fit, where low to none contributing (poorly-predictive) attributes can be safely removed without changing the overall ranking of higher attributes. This ensures that a high ranked attribute is indeed a major contributor to accurate estimation of the ultimate recovery from a field, and therefore is worth the investment for capturing its value; on the other hand, a low ranked attribute, in all likelihood, is a redundant attribute and should not be collected; this would in turn free up resources that can be allocated to acquisition of high(er) ranking attributes.

Results of this study identify attributes that are strong overall drivers in over/under - estimation of reserves in pre- and post- sanction stages. We have also ranked the key attributes to reliable EUR estimations, which should be acquired prior to commitment to sanction. In addition, a set of attributes that have been consistently ranked as poor predictors are identified, which can be safely eliminated from data acquisition without affecting appraisal accuracy. Since the database tested was substantial covering all D&UDW fields in GoM, the identified key drivers have broad coverage and application.

Abdi, H., 2003. Partial Least Squares Regression. In: The SAGE Encyclopedia of Social Science Research Methods. Sage Publications, Inc., 2455 Teller Road, Thousand Oaks California 91320 United States of America, pp. 792–795.Abdi, H., 2010. Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdiscip. Rev. Comput. Stat. 2, 97–106.Abdi, H., Williams, L.J., 2010. Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2, 433–459.Alkindi, A., 2006. Predicting Deepwater Well Behavior. In: Abu Dhabi International Petroleum Exhibition and Conference.Amirlatifi, A., Block, G., Abou-Sayed, O., Abou-Sayed, A.S., Zidane, A., 2018. OTC-28934-MS - Well Performance in New Frontiers Realizing Oilfield Big Data through Large Scale Data Analytics. In: Offshore Technology Conference. Offshore Technology Conference, Houston, Texas.Barnett, V., 1978. The Study of Outliers: Purpose and Model. J. R. Stat. Soc. Ser. C (Applied Stat. 27, 242–250.Beniger, J.R., Barnett, V., Lewis, T., 1980. Outliers in Statistical Data. Contemp. Sociol.Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J., 2000. LOF: Identifying Density-Based Local Outliers. Proc. 2000 Acm Sigmod Int. Conf. Manag. Data 1–12.Chong, I.-G., Jun, C.-H., 2005. Performance of some variable selection methods when multicollinearity is present. Chemom. Intell. Lab. Syst. 78, 103–112.Coghlan, G.P., 2010. Appraisal And Development-Sanction Challenges For A Small, Stranded, Offshore Heavy-Oil Project In The North Sea. In: Offshore Technology Conference. Offshore Technology Conference, pp. 606–614.Davis, N., Riddiford, F., Bishop, C., Taylor, B., Froukhi, R., 2001. The In Salah Gas Project, Central Algeria?: Bringing an Eight Field Gas Development to Sanction. SPE Middle East Oil Show 2.Dindoruk, B., Christman, P.G., 2004. PVT properties and viscosity correlations for Gulf of Mexico oils. SPE Reserv. Eval. Eng. 7, 427–437.Eriksson, L., Byrne, T., Johansson, E., Trygg, J., Wikström, C., 2013. Multi- and Megavariate Data Analysis Basic Principles and Applications, 3rd ed. Umetrics Academy, Umeå, Sweden.Grubbs, F.E., 1969. Procedures for Detecting Outlying Observations in Samples. Technometrics 11, 1–21.Gupta, S., Saputelli, L.A., Verde, A., Vivas, J.A., Narahara, G.M., 2016. OTC-27127-MS: Application of an Advanced Data Analytics Methodology to Predict Hydrocarbon Recovery Factor Variance Between Early Phases of Appraisal and Post-Sanction in Gulf of Mexico Deep Offshore Assets. In: Offshore Technology Conference. Offshore Technology Conference.Meddaugh, W.S., 2015. Improving Reservoir Forecasts by Understanding the Relative Impacts of Sparse Data, Reservoir Modeling Workflow and Parameter Selection, and Human Bias. In: SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, pp. 3997–4011.Nandurdikar, N.S., Wallace, L., 2011. Failure to Produce: An Investigation of Deficiencies in Production Attainment. In: SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.Ord, K., 1996. Outliers in statistical data?: BarnettV. and Lewis,T. 1994, 3rd edition, (John Wiley & Sons, Chichester), 584 pp., [UK pound]55.00, ISBN 0-471-93094-6. Int. J. Forecast. 12, 175–176.Osborne, J.W., Overbay, A., 2004. The power of outliers (and why researchers should always check for them). Pract. Assessment, Res. Eval. 9, 1–8.Reid, D., Wilson, T., Dekker, M., 2014. Key Aspects of Deepwater Appraisal. In: Offshore Technology Conference. Offshore Technology Conference, Houston, Texas, pp. 5–8.Srivastava, P., Wu, X., Amirlatifi, A., Devegowda, D., 2016. Recovery factor prediction for deepwater gulf of Mexico oilfields by integration of dimensionless numbers with data mining techniques. In: Society of Petroleum Engineers - SPE Intelligent Energy International Conference and Exhibition.Talluru, G., Wu, X., 2017. Using Data Analytics on Dimensionless Numbers to Predict the Ultimate Recovery Factors for Different Drive Mechanisms of Gulf of Mexico Oil Fields. In: SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.van de Waterbeemd, H. (Ed.), 1995. Chemometric Methods in Molecular Design, Methods and Principles in Medicinal Chemistry. Wiley-VCH Verlag GmbH, Weinheim, Germany.

You may also be interested in…

Rethinking Appraisal: Identification of Pre- and Post-Sanction Uncertainty Drivers in Deep and Ultra Deep Gulf of Mexico Fields Using Data Mining and Data Analytics

Amirlatifi, A., Mississippi State University
Mills, J. R., DeepStar Committee-BP
Abou-Sayed, O., Advantek International
Block, G., Advantek International
Abou-Sayed, A. S., Advantek International
Zidane, A., Advantek International
28945-MS OTC Conference Paper - 2018
View rights & permissions

1 download in the last 30 days
148 downloads since 2007

Other Resources

Looking for more? 

Some of the OnePetro partner societies have developed subject- specific wikis that may help.


  

PetroWiki was initially created from the seven volume  Petroleum Engineering Handbook (PEH) published by the  Society of Petroleum Engineers (SPE).







The SEG Wiki is a useful collection of information for working geophysicists, educators, and students in the field of geophysics. The initial content has been derived from : Robert E. Sheriff's Encyclopedic Dictionary of Applied Geophysics, fourth edition.