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A new look on reserves prediction of unconventional shale gas plays: moving from static parameters to dynamic, operation-based reserves’ calculation
Pratami F.L.P.a, Chandra S.a, Angtony W.a
a Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019, The Author(s).As conventional oil and gas are now in a scarcer condition than they have ever been, attentions have shifted into unconventional resources. These unconventional resources, although abundant in nature, cannot be produced by conventional means economically. Therefore, efforts must be done to calculate reserves in such a fashion that uncertainties can be acknowledged, reducing errors and inaccuracies in the process. Researchers in the past have argued that uncertainties in reserve estimation cannot be measured by a single number and as the scale of reserve grows bigger, so will the uncertainties be. Two novel methods are developed from the basis of operation-based reserves’ calculation, where development strategies namely hydraulic fracturing plans will be a deciding factor in determining reserves. These two methods are adapted from Monte Carlo-based approach, in which the first method will be using random numbers that are generated for listed properties to calculate ranged probability of reserves. The second approach is based on simple well-testing procedures that can also be referred to fracture design. The procedures are then compared to define practicality and situational capability of each method prepared. The models presented are dynamically flexible, due to the fact that they are integrated into development scenarios of the reserves.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Conventional oil and gas,Development scenarios,Development strategies,Reserves estimations,Reservoir development,Static parameters,Testing procedure,Unconventional resources[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Hydraulic fracturing,Reserves estimation,Reservoir development[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1007/s13202-019-0623-z[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]