METHOD OF DETECTION OF HYDROCARBON HORIZONTAL SLIPPAGE PASSAGES

Abstract

The present invention relates to a method of detection of hydrocarbon horizontal slippage passages comprising the following steps: (a.) slippage passage data acquisition and identification; (b.) slippage passage prediction; (c.) slippage passage characterization; (d.) slippage passage calibration; and (e.) slippage passage parameterization and modelling. The present invention also relates to the use of such a method for positioning a well bore for hydrocarbon production.

Claims

1. Method of detection of hydrocarbon horizontal slippage passages comprising the following steps: a. slippage passage data acquisition and identification; b. slippage passage prediction; c. slippage passage characterization; d. slippage passage calibration; and e. slippage passage parameterization and modelling.

2. Method of detection of hydrocarbon horizontal slippage passages according to claim 1, wherein the step of slippage passage data acquisition and identification comprises data acquisition in stratified rock.

3. Method of detection of hydrocarbon horizontal slippage passages according to claim 1, wherein the step of slippage passage data acquisition and identification comprises acquiring borehole image data.

4. Method of detection of hydrocarbon horizontal slippage passages according to claim 1, wherein the step of slippage passage data acquisition and identification comprises an acquisition of one or more of: a. density data; b. gamma ray data; c. sonic compressional data; d. fast sonic shear data; e. slow sonic shear data; and f. core data.

5. Method of detection of hydrocarbon horizontal slippage passages according to claim 1, wherein the step of slippage data acquisition and identification comprises one or more of the following steps: a. core analysis; b. bore hole image analysis; c. drilling data analysis; d. dynamic data analysis; e. seismic attribute analysis; and f. curvature/strain analysis.

6. Method of detection of hydrocarbon horizontal slippage passages according to claim 1, wherein the step of slippage passage prediction comprises one or more of the following steps: a. petrophysical review; b. determining of slippage passage potential index (SPPI); c. azimuth, edge, coherency determination and tracking; and d. curvature/strain analysis.

7. Method of detection of hydrocarbon horizontal slippage passages according to claim 1 wherein the wherein the step of slippage passage prediction comprises the step of creating a 1-dimensional geomechanics model.

8. Method of detection of hydrocarbon horizontal slippage passages according to claim 1; wherein the step of slippage passage characterization comprises one or more of the following steps: a. creating slippage passage density log and/or slippage passage spacing log for a plurality of wells; b. slippage passage aperture analysis; c. estimation of slippage passage density in-between the wells; and d. geomechanics stress analysis and/or evaluation.

9. Method of detection of hydrocarbon horizontal slippage passages according to claim 1, wherein the step of slippage passage calibration comprises one or more of the following steps: a. PLT, production data build-up time & RFT/MDT review; b. well test review.

10. Method of detection of hydrocarbon horizontal slippage passages according to claim 1, further comprising the step of slippage passage upscaling and 3-dimensional slippage passage intensity modeling.

11. Method of detection of hydrocarbon horizontal slippage passages according to claim 1, further comprising the step of generating a slippage passage field wide stochastic slippage passage network.

12. Method of detection of hydrocarbon horizontal slippage passages according to claim 1, wherein the step of slippage passage parameterization and modelling comprises one or more of the following steps: a. creating a slippage passage porosity distribution model; b. creating a slippage passage permeability distribution model; and c. creating an effective slippage passage permeability distribution model.

13. Method of detection of hydrocarbon horizontal slippage passages according to claim 1, wherein the wherein the step of slippage passage parameterization and modelling comprises the step of creating a 3-dimensional MEM and strain map.

14. Use of the method of detection of hydrocarbon horizontal slippage passages according to claim 1 for positioning a well bore for hydrocarbon production.

Description

4. SHORT DESCRIPTION OF THE DRAWINGS

[0086] In the following, preferred embodiments of the invention are disclosed by reference to the accompanying figures, in which shows:

[0087] FIGS. 1A and 1B a workflow for slippage passage detection and elements of a method of detection of hydrocarbon horizontal slippage passages according to a preferred embodiment;

[0088] FIG. 2 an exemplary 2-dimensional illustration of results of the method according the invention;

[0089] FIG. 3 shows a preferred process of generating a creating a slippage passage permeability distribution model;

[0090] FIG. 4 exemplary DFN with seismic attribute images for slippage passages; and

[0091] FIG. 5 an exemplary 3-dimensional geomechanics model, showing a slippage passages distribution.

5. DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

[0092] In the following, preferred embodiments of the invention are described in detail with respect to the figures.

[0093] FIGS. 1A and 1B seen together show a workflow 1 for slippage passage detection and elements of a method of detection of hydrocarbon horizontal slippage passages according to a preferred embodiment. The workflow 1 comprises the main steps of slippage passage data acquisition and identification 10, slippage passage prediction 20, slippage passage characterization 30, and slippage passage parameterization and modelling 70

[0094] The step of slippage passage data acquisition 10 can be performed by direct observation of well data or indirect observation of data of the surrounding of the well. It preferably comprises one or more of the following steps: [0095] a. core analysis 11; [0096] b. bore hole image analysis 12; [0097] c. drilling data analysis 13; [0098] d. dynamic data analysis 14; [0099] e. seismic attribute analysis 15; and [0100] f. curvature/strain analysis 16.

[0101] The step of slippage passage prediction 20 can be performed intra well for one specific well or inter well, regarding the relationships of a plurality of wells. It preferably comprises one or more of the following steps: [0102] a. petrophysical review 21; [0103] d. determining of slippage passage potential index (SPPI) 22; [0104] e. azimuth, edge, coherency determination and tracking 23; and [0105] f. curvature/strain analyses 24.

[0106] The step of slippage passage characterization 3o preferably comprises one or more of the following steps: [0107] a. creating slippage passage density log and/or slippage passage spacing log for a plurality of wells 31; [0108] b. slippage passage aperture analysis 32; [0109] c. estimation of slippage passage density in between of the wells 33; and [0110] d. geomechanics stress analysis and/or evaluation 34.

[0111] The step of slippage passage calibration 40 preferably comprises one or more of the following steps: [0112] a. PLT (Production Logging Tool), production data build-up time & RFT (Repeat Formation Tester)/MDT (Modular Dynamic Formation Tester) review 41; and [0113] b. well test review 42.

[0114] The method of detection of hydrocarbon horizontal slippage 1 further comprises the step of slippage passage upscaling and 3-dimensional slippage passage intensity modeling 50.

[0115] The method of detection of hydrocarbon horizontal slippage 1 further comprises the step of generating a field wide stochastic slippage passage network 60.

[0116] The step of slippage passage parametrization and modelling 70 preferably comprises one or more of the following steps: [0117] a. creating a slippage passage porosity distribution model 71; [0118] b. creating a slippage passage permeability distribution model 72; and [0119] c. creating an effective slippage passage permeability distribution model 73.

[0120] FIG. 2 shows an exemplary 2-dimensional illustration 100 of results of the method 1. In track no the reference depth of the formation under investigation is provided. Track 120 shows a UHRI dynamic image of the formation. Track 130 shows a classified heterogeneity image and track 140 a porosity image generated from calibrated UHRI image and total porosity log. Track 150 shows a porosity contribution per texture class using porosity image 140 and the classified heterogeneity image 130. Track 160 shows a connectedness curve generated for connected porosity. In the classified heterogeneity image 130 isolated porosity 132 and fracture connected porosity 134 is shown together with connected porosity 136.

[0121] The step of creating a slippage passage porosity distribution model 71 preferably uses the results of the step of slippage passage aperture analysis 32. For example, as shown in FIG. 2 a value in the porosity image 120 which is at a connected conductive spot in the heterogeneity image 130 is classified as porosity from connected conductive spot. Two types of curves are created for each heterogeneity class. The first curve 152 is the contribution of each texture category to the total image porosity, the second one 162 is the average porosity of each texture class. The textural and porosity analysis in reservoir revealed varying amount of heterogeneity in form of conductive and resistive (dense) areas across the whole interval. The conductive heterogeneities are due to porous areas (patches of intergranular and intercrystalline porosity, mouldic, vuggy porosity and slippage passages conductive intervals) of different size, shape and conductivity. The resistive heterogeneities are due to dense cemented areas of lower or zero porosity. The extracted quantitative information from BHI was used to identify several heterogeneous zones associated with higher secondary porosity and higher connectedness zones, most of the connected porosity zones were found in two units of the FIG. 2 showing one example. Track 150 shows the image extracted porosity type contributions to total porosity. The shading in track 150 indicates the contribution from each pore type. The quantitative information on the different pore types 152 and the pore connectedness index 162 is very useful for identifying the most productive zones in reservoir and to understand the correlations between various reservoir porosity components and well productivity data.

[0122] In this exemplary well of FIG. 2, a PLT survey was carried out to define the production profile. Good production contribution has been obtained from intervals where standard logs show low porosity whereas the slippage passages zones having higher porosity and responsible for the main production. Excellent correlation, however, is observed between production log profile and the connectedness log 160 derived from the borehole image 120. It is inferred that the variation in production profile is triggered by the slippage passage variation in the reservoir, i.e. zones dominated by connected slippage passages yield higher production rate whereas less rates are observed in zones dominated by other zones. Zones dominated by isolated vuggy porosity and matrix porosity have little to no contribution to production. The pore connectedness index 160 provides a significant and relevant qualitative measure to predict the producibility and can be used to optimize the completion in future wells.

[0123] FIG. 3 shows the process of generating a creating a slippage passage permeability distribution model 72 by an example of conductive heterogeneity sub-classified into fracture connected heterogeneity. The first track shows the BHI dynamic image 120. The second track shows a conductive heterogeneity image 122. The third track shows a slippage passage image 124 with fracture sinusoids 126 that have been previously picked or extracted using segment extraction methods. The fourth track shows the subcategorized heterogeneity image 130 using fracture dips conductive heterogeneity at or connected to fractures 134 (orange) and into isolated conductive heterogeneity 132 (green).

[0124] In the step of creating a slippage passage permeability distribution model 72 preferably the calibrated image, dynamic image 120 and the matrix image is used to delineate the heterogeneities. The entire image is first segmented into mosaic pieces (segments) using a well-known image segmentation method called watershed transform method as explained in Meyer, F.; Beucher, S., “Morphological segmentation” in “Journal of Visual Communication and Image Representation”, year 1990, pages 21-46. Each mosaic piece is characterized by its attributes such as the peak/valley value, contrast against matrix image, size, and type. Two mosaic types are extracted: conductive type (the mosaic pieces above matrix image) and resistive type (the mosaic pieces below matrix image). To examine the connectedness between conductive mosaic pieces, crest lines are extracted by applying the watershed transform to the original image. The crest line of the image helps identify the isolated and connected conductive features. A cut-off value is applied then on the mosaic pieces attributes (value and contrast) to extract the conductive heterogeneities (e.g. slippage passages) and the resistive heterogeneities (e.g. cemented patches). The extracted conductive heterogeneity spots 122 are subclassified into different categories 132, 235, 136. Spots connected by crest lines to another spot are classified as connected spots 136. The spots connected to slippage passages (previously extracted slippage traces and dips) are classified as slippage passages spots 126, which are the spots aligned along slippage passages are classified, and the rest are classified as isolated conductive spots 132. Size, contrast, and surface proportion of each spot/heterogeneity category are computed and represented as curves. The connectedness (is compatible to permeability) curve 162 is extracted, and it is defined by the average of the differences in conductivity between matrix and crest line (zero if there is no line) at each depth level. This curve is a very good indicator for productive zones. It is also possible here to exclude the conductive spots related to clay layers, stylolites, induced fractures and borehole breakouts using the relevant dips previously picked, such spots are classified as false porosity and it will be excluded from the porosity calculations.

[0125] FIG. 4 shows a field wide stochastic slippage passage Network 60. The acoustic impedance overlapped with bedform frequency 200 comprises on the left side the seismic attribute image 210 for slippage passages and to the right a seismic image 220 with slippage passages 222 and arrows 224 indicating slippage directions along the horizons. Generally, a stochastic slippage network model 210 is created as a basic step as attribute, along with creating the slippage passages interpretation 220 and comparing both. Then comparing the results from the BHI with the flow directions 224 of the stochastic slippage passage network 200 and those attributes related to the slippages passages 222 attributes.

[0126] FIG. 5 shows an exemplary 3-dimensional geomechanics model (MEM) with a strain map 300 obtained by a step 50 of creating a 3-dimensional MEM and strain map 300. The upscaled and extrapolated locations of extreme values of shear stress indicate the presence of potential slippage passages.