Abstract
In the context of a global energy transition, oil and gas will remain an important part of the energy mix, especially in developing countries. The challenge of energy companies is to adapt to a changing policy and investment landscape, and still remain competitive. In this work we present a generalized optimization framework for the design of oil and gas gathering networks accounting for combined shale oil and gas development strategies. We develop mixed-integer linear (MILP) and quadratically constrained models (MIQCP) to optimally determine the network of pipelines, separation, processing and delivery facilities for both oil and gas. In contrast to previous approaches, the networks are built with no predetermined number of echelons. We assume that there is a set of generic nodes to be connected among themselves to reach the final destinations. By including pressures as decisions variables, flowrates and flow directions can be optimally determined along the time horizon to make a better use of the transportation capacity. Stochastic programming extensions of the models permit to determine the network of surface facilities that maximizes the expected net present value of the project under uncertain scenarios. Oil and gas prices may significantly change in the future, leaving open the question as to whether the focus will be on developing wells producing more oil than gas, or vice-versa. Shale oil and shale gas wells usually coexist in nearby regions of the same formation. Therefore, there is a nontrivial decision on where and when to build and expand the gathering networks. We assess the potential of the formulations by solving four case studies from the unconventionals industry.
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Abbreviations
- D :
-
Alternative pipeline diameters
- E :
-
Alternative sizes for capacity expansions
- K :
-
Components in the production flow (oil, gas, water)
- R :
-
Rows of wellpads
- S :
-
Alternative sizes for processing and delivery facilities
- T :
-
Time periods
- TI \(\subseteq \) T :
-
Subset of time periods when investments can be made
- W :
-
Wellpads
- \({J}_{r}\subseteq R\) :
-
Subset of rows that can be directly connected with row r through a pipeline segment
- \({W}_{r}\subseteq W\) :
-
Wellpads comprising row r
- \(\omega \) :
-
Index accounting for scenarios in the stochastic formulations
- \({dc}_{k,e}\) :
-
Delivery capacity added by an expansion of size e for component k
- \({diam}_{d}\) :
-
Value of diameter d, usually in inches
- \(i\) :
-
Discount rate for evaluating cashflows over time
- \({idp}_{k,e}\) :
-
Installation cost of an expansion of size e for the delivery of component k
- \({ipf}_{k,s}\) :
-
Installation cost of a processing facility of size s for component k
- \({ipl}_{k,d,r,{r}^{^{\prime}}}^{P}\) :
-
Installation cost of a pipeline of diameter d for processed component k, connecting rows r and r’
- \({ipl}_{k,d,r,{r}^{^{\prime}}}^{U}\) :
-
Installation cost of a pipeline of diameter d for unprocessed component k, connecting rows r and r’
- \({isf}_{s}\) :
-
Installation cost of a separation facility of size s
- \({odp}_{k,r}\) :
-
Unit operating cost of a delivery point for component k installed in row r
- \({opf}_{k,r}\) :
-
Unit operating cost of a processing facility for component k installed in row r
- \({opl}_{k,d,r,{r}^{^{\prime}}}^{P}\) :
-
Unit operating cost of a pipeline of diameter d for processed component k moving from row r to r’
- \({opl}_{k,d,r,{r}^{^{\prime}}}^{U}\) :
-
Unit operating cost of a pipeline of diameter d for unprocessed component k moving from row r to r’
- \({osf}_{k,r}\) :
-
Operating cost per unit of k being separated in row r
- \({p}_{k,w,r,t}\) :
-
Production rate of component k from wellpad w in row r during period t
- \({pc}_{k,s}\) :
-
Processing capacity of a facility of size s for component k
- \({price}_{k,t}\) :
-
Net income per unit of component k, from the price forecasted for period t
- \({sc}_{k,s}\) :
-
Separation capacity for component k in a facility of size s
- \({tc}_{k,d}\) :
-
Transportation capacity of a pipeline of diameter d for component k
- \({\alpha }_{k}\) :
-
Conversion factor from unprocessed to processed flows of component k
- \({\Delta spp\boldsymbol{ }}_{r,{r}^{^{\prime}}}^{ Max}\) :
-
Maximum difference of processed gas square pressures between rows r and r’
- \({\Delta spu\boldsymbol{ }}_{r,{r}^{^{\prime}}}^{ Max}\) :
-
Maximum difference of unprocessed gas square pressures between rows r and r’
- \(\varphi \) :
-
Constant from Weymouth correlation for gas fluid-dynamic calculations
- \({D}_{k,r,t}\) :
-
Flow of component k delivered from a facility of row r during period t
- ENPV :
-
Expected net present value of the development project under uncertain scenarios
- \({f}_{w,r,t}\) :
-
Fraction of the production coming from wellpad w in row r that is curtailed during period t
- \({MaxFlowP}_{d,r,{r}^{^{\prime}},t}\) :
-
Maximum admissible flow for processed gas moving from r to r’ through a pipeline of diameter d during period t
- \({MaxFlowU}_{d,r,{r}^{^{\prime}},t}\) :
-
Maximum admissible flow for unprocessed gas moving from r to r’ through a pipeline of diameter d during period t
- \({NPC}_{oil/gas}\) :
-
Total net present cost of the oil/gas gathering network
- NPV :
-
Net present value of the combined shale oil and gas development project
- \({Q}_{k,r,{r}^{^{\prime}},t}\) :
-
Flowrate of unprocessed component k moving from r to r’ during period t
- \({P}_{k,r,t}\) :
-
Flowrate of component k processed in a facility of row r during period t
- \({PP}_{r,t }^{sq}\) :
-
Square pressure of processed gas flowing through row connection r during period t
- \({PU}_{r,t }^{sq}\) :
-
Square pressure of unprocessed gas flowing through row connection r during period t
- \({R}_{k,r,{r}^{^{\prime}},t}\) :
-
Flowrate of processed component k moving from r to r’ during period t
- \({\tau }_{\omega }\) :
-
Probability of scenario \(\omega \)
- \({u}_{k,d,r,{r}^{^{\prime}},t}\) :
-
= 1 If a pipeline of diameter d for unprocessed component k is installed between rows r and r’ at period t
- \({u}_{di{r}_{r,{r}^{^{\prime}},t}}\) :
-
= 1 If unprocessed gas flows from r to r’ during period t
- \({v}_{k,d,r,{r}^{^{\prime}},t}\) :
-
= 1 If a pipeline of diameter d for processed component k is installed between rows r and r’ at period t
- \({v}_{di{r}_{r,{r}^{^{\prime}},t}}\) :
-
= 1 If processed gas flows from r to r’ during period t
- \({x}_{k,s,r,t}\) :
-
= 1 If a processing facility of size s for component k is installed in row r at period t
- \({y}_{k,e,r,t}\) :
-
= 1 If an expansion of size e for the delivery of component k is installed in row r at period t
- \({z}_{s,r,t}\) :
-
= 1 If a separation facility of size s is installed in r at period t
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Acknowledgements
Financial support from National University of Litoral under Grant CAI+D 2020 50620190100163LI, and CONICET under Grant PIP 11220200103053CO is gratefully acknowledged. Financial support from ExxonMobil Upstream Research Company through the Center of Advanced Process Decision-making at Carnegie Mellon University is also gratefully acknowledged.
Funding
Financial support from University of Litoral and CONICET. Financial support from ExxonMobil Upstream Research Company through the Center of Advanced Process Decision-making at Carnegie Mellon University.
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AFM: Conceptualization, Methodology, Data Curation, Investigation, Visualization, Software, Formal analysis, Writing—review and editing. DCC: Conceptualization, Methodology, Investigation, Formal analysis, Visualization, Writing—original manuscript, Writing—review and editing, Funding Acquisition. IEG: Conceptualization, Supervision, Validation, Writing—review and editing, Project Administration, Funding Acquisition. OO: Conceptualization, Data Curation, Visualization, Validation, Resources. YS: Conceptualization, Methodology, Validation, Supervision, Resources. TZ: Conceptualization, Methodology, Supervision, Validation. YG: Conceptualization, Methodology, Validation. X-HW: Conceptualization, Methodology, Validation, Resources. KF: Conceptualization, Methodology.
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Ti Zhang: Previously affiliated to ExxonMobil Upstream Research Co, 22777 Springwoods Village Parkway, Spring, TX, 77389, USA.
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Montagna, A.F., Cafaro, D.C., Grossmann, I.E. et al. Surface facility optimization for combined shale oil and gas development strategies. Optim Eng 24, 2321–2355 (2023). https://doi.org/10.1007/s11081-022-09775-8
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DOI: https://doi.org/10.1007/s11081-022-09775-8