Big Data Analytics and Spatial Common Data Model Role

Transcription

1 476 Int'l Conf. Par. and Dist. Proc. Tech. and Appl. PDPTA'15 Big Data Analytics and Spatial Common Data Model Role Ayman Ahmed Sami a a) Senior GIS Analyst Engineer Openware (Kuwait Oil Company) Abstract - Big data analytics in terms of business perspective is the way to extract and derive new information based on analytical steps for the current raw data. Integration process to create the business logic model is one of the main challenges in this step, which is required to view raw data in new business objective dimension. This paper drills into the role of spatial common data model (SCDM) in the integration process between various workflows to derive unified data logic layer as big data analytics foundation. Through the case study, SCDM is capable to manage and analyse the basic big data dimensions (volume, velocity and variability). Keywords: Data Analytics, Spatial common data model, spatial risk model, quality performance index. 1 Introduction Margaret Rouse says that data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. Thus, raw data has to be captured in unified framework or environment to be able to analyse and manage the interaction between raw data to be able to represent it in the new business objective dimension. Integration process is responsible for interaction and communication between the various workflows to derive the target data logic layer. Ayman Sami shows how to deploy and implement spatial common data model (SCDM) in geographic information system (GIS) to act the domain model for the various workflows in the enterprise environment. SCDM is based on analysing the decomposed activities or workgroups for common processes and providing the spatial integrity or correlation between the decomposed activities, which could be established in GIS environment. The following case studies utilize this concept or methodology to develop the unified geo framework to derive decision-making information based on the integration between the current and heterogonous workflows in enterprise oil and gas industry. 2 SCDM and spatial management of emergency response plan This case drills into the development of spatial risk model which would act as SCDM to be able to spatially manage the various modules of the emergency response plan for both preventive and response actions in oil and gas industry based on the H2S dispersion model. The scope is to create dispersion risk model for H2S derived from EUB (Energy and Utility Board) dispersion calculation model. Then, calculated EPZ (Emergency Planning Zones) spatially for sour wells based on phase operation is implemented. Wind magnitude and direction impact in case of calculated protective action zone are considered. Capability of creating actual EPZ zones is based on geospatial analysis relationships. These zones are recognized with respect to the available geographic objects. For example, these objects can be the access routes and their availability. Implement the spatial risk assessment score matrix per sour well to enable the spatial management of ERP type related to the well based on its status. Proposed spatial data model for ERP management based on the spatial risk model: The proposed spatial ERP data model is based on the developed spatial risk model derived from the defined spatial and non-spatial probable parameters as well as the related consequence analysis for H2S dispersion through which the emergency level could be specified as well as related ERP modules. Different factors related to the spatial situation in question is being taking care off. Figure 3 shows proposed flow chart of spatial ERP management based on EPZ consequence analytical zones. Customized spatial risk model using python script in ESRI environment:

2 Int'l Conf. Par. and Dist. Proc. Tech. and Appl. PDPTA' The data needed are being gathered. These data are being put in the proper form for ESRI computer software environment. Figure 4 shows the customized GIS model used to extract the main output parameters from the calculated EUB (Energy and Utility Board) for H2S calculation model. Such parameters are the calculated EPZ (Emergency Planning Zone), PAZ (Protective Action Zone), IIZ (Initial Isolation Zone) distances, H2S concentration, wind magnitude, phase operation, and other mandatory parameters to be accounted for in the spatial risk model. Create spatial risk model: This model will indicate the amount of risk involved depending on the probability analysis needed. The spatial EPZ, PAZ, IIZ per analytical asset or element (sour well) is being created. Then, the spatial risk model can be developed based on EPZ consequence analysis and the defined impact parameters. Figure 5 shows the initial output of the spatial risk H2S dispersion model based on the calculated EPZ zones from EUB for H2S dispersion model, as well as the creation of initial spatial risk model. The final resized EPZ, PAZ, IIZ with the modified spatial risk model: The final spatial model will detect and adapt to changes. Figure 6 shows how the spatial model is intelligent enough to detect any change in the defined risk parameters whether spatial, environmental (e.g., wind direction and magnitude from sensors, etc.) or non spatial risk parameters (e.g., uncontrolled flow, etc.) and adapt accordingly. 3 SCDM and pipeline asset management integrity This case drills into the utilization of APDM (ArcGIS of Pipeline Data Model). ADPM is GIS template derived from PODS (Pipeline Open Data Standard) which enables GIS specialist to be able to easily manage the basic elements for pipeline asset management integrity in GIS environment. We rely here on the spatial integrity and relationship between 3 main basic elements which are : control points, station series and risk analysis layers to develop SCDM based on the derived spatial risk model from the mentioned relationship. Figure 7 shows GIS QPI (Quality Performance Indicator) surface created based on the polynomial relation between the risk and the quality. Figure 8 shows how SCDM can be utilized to detect the least QPI areas and detect the main events with its related consequence which lead to low QPI.This could be achieved through the spatial analysis and management of the integration process between the heterogonous workflows in pipeline asset management. 4 Equations for Spatial Risk Score Matrix Development and ERP Spatial Management 1) Refined EPZ radius = EPZ calculated (1 + Related consequence value). [1] The actual EPZ calculated based on the impact of spatial and non-spatial parameters on the consequence matrix. 2) (Total Of Risk) TOR = (Probability * Consequence) per asset or analytical element. [1] [ 2]. The total of risk score per asset (object). This value is inserted into the risk score matrix for risk evaluation. 1 (x μ) 2 /2σ f(x)= e 3) 2πσ. [ 4] Binomial distribution equation: that represents the probability relationship between the quality performance and related risk. This equation is used for the calculation of quality performance indicator of the target plan. TOR where 2, 2 TOR and 4 X = 1 (Assumed Random Variable) Scale Factor = 100 (Assumed) 4) QPI (Quality Performance Index) [ 3] f(x) 100 QPI of target plan is calculated assuming that the scale factor is 100.

3 478 Int'l Conf. Par. and Dist. Proc. Tech. and Appl. PDPTA'15 5 Figures Figure 1: The proposed total of risk score matrix for spatial ERP Figure 2: Phases identified in a typical emergency management process

4 Int'l Conf. Par. and Dist. Proc. Tech. and Appl. PDPTA' Figure 3 Proposed flow chart of spatial ERP management based on EPZ consequence analytical zones. Fogure 4 The model interface of spatial H2S dispersion model.

5 480 Int'l Conf. Par. and Dist. Proc. Tech. and Appl. PDPTA'15 Figure 5 The calculated spatial EPZ zones. Figure 6 The refined spatial EPZ zones.

6 Int'l Conf. Par. and Dist. Proc. Tech. and Appl. PDPTA' Figure 7 Spaial interpolated QPI surface Figure 8 Rquired information for the Low quality cause based on spatial analytical of data sources

7 482 Int'l Conf. Par. and Dist. Proc. Tech. and Appl. PDPTA'15 6 References [1] British Columbia Oil and Gas Handbook Emergency Planning and Requirements for Sour Wells,. Technical guideline documentation from BC Oil and Gas Commission. [2] The Canadian Association of Petroleum Producers (CAPP), CAPP Companion Planning Guide to ERCB Directive 071,. Technical guideline documentation from the Canadian Association of Petroleum Producers (CAPP). [3] A. Sami, Role Of Geographic Information System For Asset Management Information Risk Assessment For Pipelines Utility In Oil And Gas Industry, ESRI, UC, July [4] A. Sami, Spatial ERP Management for oil and gas, IARIA,Geoprocessing, Lisbon February