Sweet Spot Analysis Using Nonlinear Neural Network with Multivariate Input and Multivariate Output

Transcription

1 GUSS14 - # # # Sweet Spot Analysis Using Nonlinear Neural Network with Multivariate Input and Multivariate Output TOM COX*, IVAN PRIEZZHEV, AARON SCOLLARD, and ZHENGANG LU Schlumberger Information Solutions This paper has been selected for presentation for the 2014 Gussow Geosciences Conference. The authors of this material have been cleared by all interested companies/employers/clients to authorize the Canadian Society of Petroleum Geologists (CSPG), to make this material available to the attendees of Gussow 2014 and online. ABSTRACT The identification of sweet spots in unconventional and resources development plays is a method for focusing efforts on the areas of best potential. These plays often have an abundance of diverse data sets that can be utilized; seismic data (amplitudes, inversions, rock properties, etc), regional maps (gravity, magnetics, stress maps, etc), production information (oil, gas, water rates, etc.), and development parameters (horizontal length, azimuth, fracture design, etc). Combing the information sets requires a multi-variant approach. Neural networks are well suited for predicting multiple parameters due to their ability to simultaneously predict several variables, their lack of sensitivity to highly correlated inputs, and the ability to incorporate non-linear relationships. Using seismic, gravity, and magnetic maps production potential is predicted at an edge of the historical Pembina Cardium field where drilling has evolved from vertical wells in a pattern, to horizontal well drilling with stimulation, following the techniques employed in the unconventional plays. The prediction technique uses a neural network to predict production parameters. The actual well production is conditioned to account for varying lengths of horizontal wells, and for varying success in accessing the geologic potential. The neural network is trained with the production data to predict production rate. The prediction compares very well with input data achieving a correlation. INTRODUCTION Development of resource plays are capital intensive projects. In today s unconventional play and resource developments, with high horizontal well counts and extensive stimulation efforts, the results do not always meet expectations with up to 40% of unconventional Eagle Ford Shale wells being uneconomic (Source: IHS and PFC Energy, taken from Shorn, 2014). Being able to identify the better potential locations or sweet spots could help to focus efforts where there is best chance of commercial success. There is an abundance of relevant data to facilitate this, from well logs and seismic to completion and production information, and the integration 1

2 of these diverse data sets with varying scales can be approached with different methodologies (Deutsch, 2013). Well performance varies around a field; it is not randomly distributed, but grouped around sweet spots (PFC Energy 2012). This is demonstrated in the Bakken quality map in Figure 1. Identification of the sweet spots can help focus on areas of higher potential production. Figure 1: Production quality map of the Bakken. Coloured by quintiles of peak BOE/lateral ft. From PFC Energy A well s production performance is dependent on three main factors: 1. The geologic reservoir potential of the location where the well is drilled. 2. The positioning of the well bore to access the maximum potential of that location. 3. The success of the completion to connect the well bore to the reservoir potential. The first factor defines the maximum a horizontal well could produce provided that it drilled the best reservoir zone for its entire length and the completion process was successful at connecting the reservoir to the well bore. Factors 2 and 3 will reduce the real production of the well if they are not 100% successful, and result in a lot of noise in the production numbers making it more difficult to identify the geologic reservoir potential. In this paper we are applying a multivariant technique developed to identify sweet spots for unconventional resources in the conventional oil field of the Pembina Cardium field, in west central Alberta. The prediction method is based on an analysis of the relationships between independent variables such as seismic, gravity, and magnetic data, as well as other geological and geophysical information (maps or 3-dimensional distributions), with production data averaged over time. In order to predict beyond well control it is desirable to use data types that have areal coverage and are not directly related to production. A neural network is the main predictive engine. The primary advantages of this are as follows: 1. The ability to simultaneously predict several variables such as oil, gas, and water production rates, as well as engineering parameters correction (length and azimuth of horizontal part of well, number of fracture jobs stages, etc.). 2. A lack of sensitivity to the correlation between input attributes. If some inputs are highly correlated, the neural networks can automatically compensate through thresholds and input coefficients. 3. The degree of non-linearity of the relationships can be managed through the dimension of the hidden layer. If there is no hidden layer, the neural network searches for a linear relationship. 4. The iterative search is based on evolutionary algorithms that find a solution very close to a global minimum. 5. Simultaneous prediction for multivariate outputs through the minimization of the square difference, together with the cross correlation between predictive output parameters (oil, gas, and water rates). This allows for prediction without the influence of strong correlations. Pembina Cardium field The Pembina Cardium field was first drilled in 1953 and is one of the largest and most prolific conventional oil fields of Western Canada. It is located in west central Alberta around township 48, range 5, west of the 5th meridian (see Figure 2), and has had a low recovery percentage of 20% making it attractive due to the considerable resource that remains (Krause et al, 1994). The application of horizontal drilling and horizontal fracture stimulation in the field is reviving this 50- year-old asset (ARC Resources Ltd., 2014). The field has historically been produced with vertical producers and injectors with the more recent horizontal drilling around the field margins as seen in the study area map of Figure 3. 2

3 Figure 2: Pembina Cardium wells of west central Alberta (map generated from IHS Accumap ). Figure 4: Multi input and multi output neural network with one hidden layer. Classic predictive analysis uses only one output and during the learning stage does a minimization of the difference between measured and predictive values. Usually the distribution of this difference assumes a Gaussian distribution, and requires minimizing the square difference to achieve it. In our case, for multi outputs, it is not enough to minimize just the square difference because with a multivariate Gaussian distribution the objective function has to include the cross correlation between the output datasets. This difference is further explained mathematically below. If vector, 1,, defines a predictive parameter (for example average one year oil rate), where N is the Figure 3: The study area showing the migration of vertical well to horizontal wells at the field edge. The pie charts represent the first 3 months of oil, gas, and water for each well (E^3m^3). number of wells used for learning, and, 1,, is the predicted parameter, then the probability density according to a Gaussian distribution will be the following: The data set used was gathered from public data available from IHS Canada, a public source of gravity and magnetic data, and a stacked and migrated seismic volume available from WesternGeco. (1) To maximize (1) it is enough to minimize just the square METHODOLOGY difference Multi-variant technique The technology is based on a non-linear neural network (see Figure 4) (Priezzhev et al, in press) that can be built using a multivariate Gaussian distribution theory, which allows for a simultaneous prediction of several parameters (for example: oil, gas, water rates). The learning stage for neural networks is usually based on a learning dataset, which in our case is a set of wells with average production rates.. For the multivariate Gaussian distribution, are different for each i-well and each k-predicted parameter. 3 =! % " # # $ & ' " # # $ (2)

4 To maximize (2) we needed to minimize:, (3) Figure 6 shows the Cardium amplitude map with an approximate visual correlation to the drilled wells. where S is a cross-correlation matrix between output parameters (for example oil, gas, and water rates). We use function, usually called Mahalanobis distance (Mahalanobis, 1927), as an objective function during neural network learning. The operator is built during the learning stage based on the multivariate dataset which may contain: 1. 3D seismic 2. Surface or attribute maps 3. Gravity fields 4. Magnetic fields 5. Production data (oil, gas, and water production rates normalized to a defined period) A 2D moving window helps to build a more stable operator. The output results are a map of the prediction variable (production rates). Initial geological and geophysical interpretation The gamma ray and resistivity logs were used where available (307 wells) to standardize the top picks for the CRDZ (top of Cardium zone) (Krause 1994), CRDM (top of Cardium sand) and BLCK (Blackstone beneath the Cardium). An additional internal event was also picked to define the base of the major sandy sequence in the Cardium. These events were structurally mapped and quality checked for wells with picks that had obvious issues or data problems, which were discarded. The Cardium seismic event was interpreted with the use of synthetic ties for wells containing sonic and density curves and validated with the Geophysical Atlas of Western Canadian Hydrocarbon Pools (Viney and Chappell, 1989). The LPRK (Lea Park), CLRD (Colorado), CRDM and VKNG (Viking) events were picked on the seismic volume. Wren (1984) suggested the Cardium could provide an amplitude response with careful processing, and that maximum amplitude could be found with intermediate offsets that could indicate reservoir presence, however it was not a strong amplitude response and he was optimistic that the cretaceous reservoir would become resolvable with processing improvements. Figure 5: Cardium amplitude map, notice the approximate visual correlation with the Cardium drilled reservoir wells. Given the amount of well control, a layer cake depth conversion model for the seismic was created by generating interval velocities that matched the seismic time interpretation surface to the depth well top picks. The LPRK, CRDM, and VKNG events and tops were used. This domain conversion model was used to convert the various seismic volumes to depth. The seismic volume was inverted to an acoustic impedance volume using the whitening inversion technique described in Priezzhev, Amplitude slice maps were extracted parallel to the CRDM surface from the amplitude and acoustic impedance volumes at various offsets from the surface. These attribute maps, along with regional gravity (Sandwell and Smith, 2009; Sandwell et al, 2013), and magnetic (Maus et al, 2009) maps, provide the input data for the prediction of production. Production data preparation The publically available production data is generally quite noisy and needs preparation in order to provide useful training information. It is generally accepted that the best production occurs at the start of a well s history and the best indicator is the maximum rate achieved. Challenges of the public data are: Production is allocated to the well head location instead of the subsurface position. 4

5 First month of production and first three months of production need to substitute for maximum initial production rate. How the production is distributed across the perforations is not known. A well with good reservoir potential may not perform due to completion or gathering system constraints. To address some of these issues the production information has been prepared by distributing the production along the horizontal section of the well and dividing by the length to get a production per meter value, positioned at the downhole location of the wells estimating the amount of the well accessing the reservoir to produce a quality factor for each well. (The ratio of in zone vs out of zone). This is used to increase the production per meter of wells that did not access as much reservoir estimating the maximum production potential around the well by looking at the rates of the neighbouring well points. This attempts to remove possible drilling and completion chokes on production and represent the geologic potential of the reservoir. INPUTS AND RESULTS Figure 6 shows the CRDM structural depth surface and Cardium well locations, plus the 3D seismic data location along with production points. Production information is assigned to points along the horizontal section of the well, normalized to production per meter of length, adjusted for the quality of the well position in the reservoir, and adjusted to a maximum production number for a radius around the point. The analysis was run with input from 3 seismic amplitude maps, and 3 seismic acoustic impedance maps, extracted at depths of 2m, 6m, and 10m beneath the CRDM event and with gravity and magnetic maps (figure 7). Figure 6: CRDM structure map with CDRM production wells. Rectangle shows 3D seismic area. Brown diamonds show where the production is assigned to the horizontal wells. Figure 7: Sample input maps of (clockwise from top left) gravity, CRDM seismic amplitude (2m below), CRDM acoustic impedance (2m below), and magnetic strength. Note: Black contours are depth structure; the maps are rotated from north. A neural network with 3 hidden nodes was used with the input data set and trained against the prepared production data of First 3 months production per meter of horizontal well length in the reservoir. Half of the training data was used for cross validation. This allows the algorithm to run multiple realizations of the output at each predicted location. 100 realizations were selected. The mean, P10, P50, P90 are produced. Figure 8 shows the resultant predicted map of production from the Cardium. Only the horizontal wells were used in the creation of the map and it suggests some smaller potential in the northeast (where vertical wells have been drilled and to the largely, undrilled southwest). To quantify the fit of the prediction, Figure 9 shows the cross plot of input data production points with the P50 prediction at the same position. The correlation coefficient is a strong

6 The goal is to predict the geologic potential of an area, independent of drilling or completion influences. To separate the geologic potential, the production training data needs to be conditioned. The conditioning is important to achieving a strong correlation of the predicted parameter with the training data. Figure 8: Multi-variant neural network predicted oil production rate P50 of 100 realizations (units of m^3/m length). Since only horizontal wells were used in the training data set, the predicted yellow areas to the northeast and southwest can be qualitatively compared with the existing production from vertical and horizontal wells. Figure 10 shows the production pie charts sized on production per horizontal meter. The pie size has been clipped to 6 m^3/m to reduce the domination of the vertical wells. The small diamond symbols represent the position of the training/learning data. Notice that larger pies are present on the horizontal wells found in the red area of the prediction, and smaller pies in the green areas. While the prediction suggests good potential to the southwest, the few wells in that area do not support that prediction. In this area, the nonlinear neural network model is being used to extrapolate away from the known data. The use of model extrapolation should always be considered cautiously. In addition to the extrapolation of the nonlinear relationships, there are increased uncertainties associated with velocity modeling, and vertical well positioning away from the control points. CONCLUSION The presented methodology shows the application of diverse multi-variant inputs to predict production parameters that are not directly related. A neural network with multiple hidden layers can be used with multiple inputs trained to multiple output variables. Figure 9: Fit of multi-variant neural network prediction of production to the prepared input production for the first 3 months (units of m^3/m). Correlation coefficient of Figure 10: Predicted production map with actual production pie display and point locations of the input training data. The pies have been clipped to a size of 6 m^3/m of horizontal length to reduce the vertical well visual dominance. In this paper the edge of the historical Cardium field was examined via seismic derived maps, gravity, and magnetic maps. The inputs were trained against production parameters to produce predictive maps of production 6

7 potential. These can be useful in identifying sweet spots for future development. ACKNOWLEDGMENTS The authors thank Schlumberger for the opportunity to develop and present this paper. We also thank WesternGeco for allowing Schlumberger Information Solutions access to the 3D volume used in this study. The well and production data was provided via Accumap from IHS Canada. REFERENCES Deutsch, C.V Seven paradigms of data integration in reservoir modeling, CSPG Memoir20 Closing the Gap, p3-12 Shorn, P Schlumberger presentation at: Simmons & Company Energy Conference, Gleneagles, Scotland. Date accessed URL: 26_schorn_simmons.aspx PFC Energy North American Onshore Service North American Unconventional Oil and Gas: And Now for the Hard part? Rice Global E&C Forum. URL: Krause F.F., Deutsch K.B., Joiner S.D., Barclay J.E., Hall R.L. and Hills, L.V Cretaceous Cardium Formation of the Western Canada Sedimentary Basin In: Geological Atlas of the Western Canada Sedimentary Basin. Mossip G.D. and Shetsen I. (comp.), Canadian Society of Petroleum Geologists and Alberta Research Council. Date accessed URL: html ARC Resources Ltd Corporate Website. Data accessed URL: (comp.), Canadian Society of Exploration Geophysicists and Canadian Society of Petroleum Geologists. Priezzhev I. and Scollard A Robust one-step (deconvolution + integration) seismic inversion in the frequency domain. Proceedings of Society of Exploration Geophysicists Annual Meeting Las Vegas URL Priezzhev I., Scollard A. and Lu Z. Regional production prediction technology based on gravity and magnetic data from the Eagle Ford formation, Texas, USA. Submitted to Society of Exploration Geophysicists Annual Meeting Denver Mahalanobis P.C Analysis of race mixture in Bengal. Journal and Proceedings of the Asiatic Society of Bengal, v23, p Sandwell, D. T., and Smith, W.H.F Global marine gravity from retracked Geosat and ERS-1 altimetry: Ridge segmentation versus spreading rate. Journal of Geophysical Research, v114, B01411 Sandwell, D. T., Garcia, E., Soofi, K., Wessel, P. and Smith. W.H.F Toward 1 mgal accuracy in global marine gravity from CryoSat-2, Envisat, and Jason-1. The Leading Edge, v32, p Maus, S., Barckhausen U., Berkenbosh, H., Bournas, N., Brozena, J., Childers, V., Dostaler, F., Fairhead, J.D., Finn, C., von Frese, R.R.B., Gaina, C., Golynsky, S., Kucks, R., Luhr, H., Milligan, P., Mogren, S., Muller, R.D., Olesen, O., Pilkington, M., Saltus, R., Schreckenberger, B., Thebault, E. and Caratori Tontini, F. 2009, EMAG2: A 2 arc min resolution Earth Magnetic Anomaly Grid compiled from satellite, airborne, and marine magnetic measurements. Geochemistry Geophysics Geosystems, v 10, I 8, Q Wren, A.E Seismic techniques in Cardium Exploration. Journal of the Canadian Society of Exploration Geophysicists, v20, n1, p Viney P. and Chappell J.F Chapter 9 Upper Cretaceous Reservoirs In: Geophysical Atlas of Western Canadian Hydrocarbon Pools. Annderson N.L., Hills L.V., Cederwall D.A., Greenwood E.V. and Ulaszonek B.J. 7