An empirical approach to estimating hydrocarbon column heights for improved pre-drill volume prediction in hydrocarbon exploration

28 Estimating pre-drill volumes in hydrocarbon exploration involves dealing with 29 geological and technical uncertainties. The prediction of the hydrocarbon column 30 height is widely recognized as the primary driver of uncertainty in volumetric 31 estimates. The oil and gas industry continues to renew efforts to limit such 32 uncertainties because of the potential economic costs of inaccurate estimation, yet 33 estimation of pre-drill volumes remains an in-exact science. This study introduces 34 new empirical data from the Norwegian Continental Shelf, and aims to improve 35 accuracy in hydrocarbon column height prediction. We use column height, trap height 36 and burial depth data to calculate the degree of hydrocarbon trap fill for each of the 37 242 studied discovery wells. The data is aggregated into a simple forward probability 38 model to calculate the probability of encountering different ranges of trap-fill, based 39 on burial depth and trap height. The distribution of trap-fill ratios clearly correlates 40 with both trap height and burial depth, thus indicating that the same pre-drill column 41 height distribution should not be used for all prospects. These findings strongly 42 suggest that the prospect’s dimensions and burial depth are used alongside other 43 technical subsurface factors to determine the most suitable pre-drill hydrocarbon 44 column height distribution. This method contributes to reducing the largest source of 45 uncertainty, which in turn reduces the overall uncertainty associated with pre-drill 46 volume estimation. Such an approach will increase the accuracy of pre-drill volume 47 estimation, leading to more appropriate future development plans. We recommend 48

This manuscript is a preprint and will be submitted to AAPG Bulletin. This manuscript has not undergone peer-review. Subsequent versions of this manuscript may have different content. If accepted, the final version of this manuscript will be available via the 'Peer-reviewed Publication DOI' link on the right-hand side of this webpage. Please feel free to contact Isabel directly or to comment on the manuscript using hypothes.is (https://web.hypothes.is/). We welcome feedback! estimation, leading to more appropriate future development plans. We recommend 48 that the presented methods and lessons learnt are applied in basins settings 49 worldwide.

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Estimating pre-drill volumes is an essential part of the exploration process that 52 determines if a prospect contains a large enough volume of hydrocarbons to justify 53 drilling an exploration or appraisal well. Yet, despite its importance and an overall 54 improvement in available technology, the industry continues to be poor at estimating 55 pre-drill hydrocarbon volumes (Milkov, 2017). This is largely the result of uncertainty 56 associated with each of the ten main inputs required to calculate potential 57 hydrocarbon volumes (Table 1). Combining these inputs further compounds the level 58 of uncertainty resulting in errors, which can lead to surprises and often 59 disappointment once volumes are reassessed after drilling (Garb, 1988;Skaar et al., 60 2000;Demirmen, 2007). Of all of the inputs, the hydrocarbon column height 61 uncertainty generally has the largest impact on the calculated pre-drill volume range 62 (Fig. 1). Despite this, efforts to constrain the column height are commonly bypassed 63 in favor of work that focuses on other inputs required to calculate pre-drill volumes 64 that carry far less uncertainty. Instead, we argue that constraining the hydrocarbon The oil and gas discoveries used in this study are distributed across the NCS in three 120 different regions: the Barents Sea, the Norwegian Sea and the Northern North Sea 121 above 61°N (Fig. 4). Of the 242 discoveries, 123 are located in the Norwegian Sea, 122 81 in the Northern North Sea and 38 in the Barents Sea. The fields and associated 123 structural elements that have been measured as part of the study are summarized in 124 Table 2. 125 The NCS was selected for several reasons. Firstly, it is a good area to study the 126 relationship between trap geometry and hydrocarbon column heights because the 127 majority of the basins in the study areas have received plentiful hydrocarbon charge, 128 so charge limitation is not a significant issue (Doré and Jensen, 1996;Ostanin et al., into depth. Top reservoir depth maps were crosschecked against well tops to assess 145 if any depth shift was required. Access to such maps and well data ensures that the 146 relevant data required to calculate the column height, trap height and burial depth is 147 widely available across most of the NCS. However, the assimilation and quality 148 control of the data, which is necessary to measure the apex and spill point depths, is 149 more time consuming. Figure 5 shows an example of such data collection for 150 discovery wellbore 7121/7-1, which is part of the Snøhvit field in the southwestern 151 Barents Sea (Linjordet and Olsen, 1992). The top reservoir surface was mapped in

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As with all subsurface interpretation, and in particular depth conversion, we 159 acknowledge that there will be inaccuracies associated with the apex and spill point 160 depths for individual discoveries (Etris et al., 2001;Pon and Lines, 2005). However, 161 such inaccuracies are minimized in this study through local reinterpretation of 162 regional surfaces to increase topographic detail over the structures, as well as tying 163 the depth surfaces to the relevant well-tops after depth conversion. Consequently, 164 confidence in the collection methods and resulting dataset is good.

166
The empirical data collected in this study is presented in a series of graphs that show 167 the range and distribution of measured variables, which includes the trap height, burial depth, column height and trap-fill ratio for each discovery. Cross plots are 169 useful to identify potential correlations between particular variables and therefore 170 demonstrate possible controls on the observed hydrocarbon column heights. In 171 addition, trap-fill ratios calculated for each measured discovery are displayed as 172 proportionally sized data points on regional geological maps.  Figure 6A represents the one-to-one relationship between trap height and 183 column height. All points that fall on this line have a trap-fill of 100%, and the 184 structures are considered to be "filled-to-spill". Figure 6B (Miller, 1989). 269 Burial depth was divided into three bins: 0-1500m, 1501-3000m and >3000m (Fig   270   10G), and the trap height was also divided into three bins: 0-150m, 151-300m and 271 >300m (Fig. 10H). The trap-fill ratio was divided into four bins corresponding to 0-272 50%, 51-75%, 75-99% and 100% trap-fill (Fig. 10I). 100% trap fill is assigned its own 273 bin since nearly half of the measured discoveries (111/242) recorded this level of  Furthermore, the probability of recording 100% trap-fill is approximately twice as 290 likely as recording a trap-fill ratio between 0 and 99%.

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 For trap heights between 151 and 300m, the distribution of trap-fill ratio is less 292 consistent at different burial depths. When the burial depth is 1500m or less, the 293 most likely trap-fill range is between 51 and 75% and the least likely trap-fill is 294 100%. However, when the burial depth exceeds 1500m, this pattern is reversed 295 and 100% trap-fill becomes the most likely outcome, whilst 0-50% trap-fill 296 becomes the least likely.

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 For trap heights exceeding 300m with a burial depth of less than 1500m, 0-50% 298 trap-fill is most likely and there are no discoveries with a trap-fill ratio above 75%.

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Discoveries with trap heights exceeding 300m at depths of 1501-3000m are most 300 likely to have a trap-fill ratio of 51-75% and least likely to have 100% trap-fill.

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However, when the burial depth exceeds 3000m, 100% becomes the most likely 302 trap-fill ratio and 0-50% the least likely.  structure is more likely to be able to hold a taller hydrocarbon column than a low-317 relief structure. However, a high-relief structure at shallow depths is significantly less 318 likely to be filled-to-spill than those at greater depths (Fig. 11). A weaker positive 319 correlation exists between the column height and burial depth suggesting that the 320 burial depth exerts some control on column heights, but that the relationship between 321 these variables is more complicated. The correlation between column height and 322 burial depth appears to be strongest in shallower discoveries, as shown by the 323 relatively high correlation coefficient for the Barents Sea (Fig. 7).

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The 3x3 matrix (Fig. 11) clearly shows that the observed distribution of trap-fill ratios The data used in this study is collected from a range of basins located on the NCS.

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Nevertheless, we recommend that the trends and lessons learnt in this study be 362 applied to basins and prospects that lie outside the NCS. The hydrocarbon column 363 height distribution will always remain an uncertainty when calculating pre-drill 364 volumes, regardless of where the prospect is located. This approach also has the 365 wider benefit of improving the efficiency of the exploration and production of 366 hydrocarbons, which will ultimately help to drive down costs. Improved placement of 367 wildcat, appraisal and development wells will lead to a reduction in the number of 368 redundant wells, helping to reduce emissions that are emitted during the drilling 369 process. This in turn will reduce the negative impact of drilling on the environment, 370 which is timely given the oil and gas industry is increasingly seeking ways to improve 371 its environmental image.  Statistician, v. 51, p. 59-64, doi:10.1080Statistician, v. 51, p. 59-64, doi:10. /00031305.1997

Input
Definition Hydrocarbon column height The height of a continuous hydrocarbon column measured from the apex of the structure down to the hydrocarbon-water contact Recovery factor The percentage of hydrocarbons that can be produced from the volume of hydrocarbons initially in place Net/gross ratio The reservoir rock thickness that has sufficient porosity/permeability from which hydrocarbons can be produced divided by the total reservoir thickness (see below for definition) Gas fraction of column height Proportion of the hydrocarbon column height occupied by gas Porosity (effective) Interconnected space within the rock that can be occupied by moveable fluids. Excludes isolated pore spaces.

Reservoir thickness
Thickness of the stratigraphic unit that contains the reservoir beds Oil saturation Fraction of the pore space occupied by oil Area Areal extent of the reservoir contained within the closure at the depth of the fluid contact Formation volume factor (oil) The oil volume at reservoir conditions divided by the oil volume at standard conditions (surface conditions) Depth-dependent area Function that describes the relationship between depth and the areal extent of the reservoir contained within the closure