Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d. RESERVOIR MANAGEMENT: WATER DRIVE OPTIMIZATION

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1 RESERVOIR MANAGEMENT: WATER DRIVE OPTIMIZATION

2 OPTIMIZE PRODUCTION WITH INTELLIGENT WELL MANAGEMENT 1. Sample 2. Explore 3. Uncertainty 5. Matching 4. Probabilistic Analysis 6. Strategy Optimization 7. Probabilistic Strategy 8. Development Strategy 9. Risk management

3 FUNCTIONAL DATA ANALYSIS Traditional DCA Probabilistic methodology Well Forecasting Solution Bootstrapping module Clustering module Data mining workflow

4 CLUSTER ANALYSIS 1. Cumulative liquid production 2. Cumulative oil or gas production 3. Water cut (Percentage determined by water production/liquid production) 4. B exponent (Decline type curve) 5. Initial rate of decline 6. Initial rate of production 7. Average liquid production Paper Data Mining Methodologies enhance Probabilistic Well Forecasting K.R.Holdaway

5 BIG DATA BIG ANALYTICS APPROACH Modeling Design Parameters: It has been decided that the analysis should be designed according to the following criteria: Normalization Water cut curves normalization based on Cumulative Liquid produced. Spatial Distribution Analysis should take into account increments wise regions. (there are 3 increments) Time Windows Analysis should be based on the different time phases (time windows) Water Distance Water distance (minimum FWL Distance) should be considered as one of the inputs in the clustering Well Bore Type Well bore type (horizontal or vertical) should also be taken into account

6 BIG DATA BIG ANALYTICS APPROACH NORMALIZATION OF THE WATER CUT CURVES In order to make valid comparisons between well data sets it is necessary to perform a remediation step that entails a robust quality control work process to identify outliers and impute for missing and erroneous values. Normalization Well 158_1 Normalizing WCT curves for each well using Cumulative Liquid produced in this well We refer to the normalized scatter plots of the Water Cuts as the Water Cut Marks of the wells Well 1436_0 A Water Cut normalization step is implemented based on Cumulative Liquid production. That approach helps eliminating temporal aspect of the data to some extent

7 BIG DATA BIG ANALYTICS APPROACH TAKING INCREMENTS OF HRDH INTO ACCOUNT Spatial location of the well will play an important role in the clustering analysis. We assessed the location with respect to increments Spatial Distribution Increment 1 Increment 2 Increment 3 The field is divided into three increments, however increments are correlated with production time windows. We analyzed the distributions of the wells into the increments in different time windows, and consolidated into one categorization where the clustering analysis should be based on.

8 BIG DATA BIG ANALYTICS APPROACH AVOID BIAS IN COMPARING DIFFERENT WELLS Production has started in different decades in different wells. There are wells that initiated oil production since 1960s.Therefore production amounts are in different scales for the old and new wells, and behaviors are different in the depletion, injection and post-injection phases Production Starting Time Time window 1 Time window 2 Time window 3 1 Jan 96 1 Jan 96 1 Oct Oct 2003 Time Windows It has been suggested to use the above time windows for the clustering analysis. A separate segmentation model will be developed for each of the three time windows. As note in the spatial distribution section, the well locations are depending on the increments, and increments are depending on time. Therefore it turns out that by considering only the time windows one will also incorporate the effect of the increment information

9 BIG DATA BIG ANALYTICS APPROACH DISTANCE TO WATER LEVEL IN A WELL INFLUENCES WATER CUTS The minimum distance to free water level is an important factor to understand water cut behavior of a well. Considering the wells in the region the majority of the wells are distributed within interval (Mean: 5533 Median: 5388). Distribution of the Minimum Distance to Free Water Level An interesting research question would be to analyze the different behaviors of the wells having significant different FWL Dist values Water Distance

10 BIG DATA BIG ANALYTICS APPROACH HORIZONTAL AND VERTICAL WELLS BEHAVE DIFFERENTLY Majority of the wells are horizontal and are analyzed in Time Window 3. There are also a small number of wells having deviated configuration Time Window Window1 ( ) Window2 (1996 Oct.2003) Window3 (Oct ) Number of Horizontal Wells Number of Vertical Wells Deviated Wells Well Bore Type Distribution of wellbore type for the wells considered throughout the analysis

11 WELL CONFIGURATION (HORIZONTAL / VERTICAL) Distribution of Well Configuration (Horizontal / Vertical) in the Clusters As it can be seen the distribution is characteristic for some clusters. Clusters are represented by their numbers *Deviated Wells Excluded All wells in this cluster are vertical

12 RESULTS / GENERAL COMMENTS PRESSURE TRENDS (TIME WINDOW 1) SWP Annual Average SWP for the two clusters of Time Window In the early years of the production both clusters are following the overall trend of the wells in the time window. After 2003 the annual SWP average for the wells in cluster 2 starts increasing and surpasses that of cluster 1 although the SWP trend of both clusters is positive (probably due to injection) Year

13 RESULTS / GENERAL COMMENTS PRESSURE TRENDS (TIME WINDOW 2) SWP Annual Average SWP for the two clusters of Time Window In the early years of the production the clusters are following the overall trend of the wells in the time window Different Average SWP trends based on most recent observations After 2003 and Injection period the pressure is measured differently for the wells in cluster 4. That cluster has the largest pressure. It is almost 3500 on the average Year

14 RESULTS / GENERAL COMMENTS PRESSURE TRENDS (TIME WINDOW 3) SWP Annual Average SWP for the two clusters of Time Window Since the wells in this time window are relatively new and the production started in 2003, the pressure is following a steady trend However, it can be noted that first and second clusters are in the declining phase, whereas 3,4 and 5 are increasing Year

15 OPTIMIZE PRODUCTION WITH INTELLIGENT WELL MANAGEMENT

16 OPTIMIZE PRODUCTION WITH INTELLIGENT WELL MANAGEMENT

17 Q&A ENERGY ANALYTICS SUMMIT 2014