geothermal react example chat

gistfile1.txt

>>> can you help me do a geo power analysis
⠙ thinking...[2024-08-23 11:05:30,632] {_client.py:1026} INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
thought: I need to gather the necessary parameters for the geo power analysis.
tool: geo_power_api
tool_input: {'data': {}}

observation: {'1_user_inputs': {'application': 'power', 'latitude': None, 'Longitude': None, 'subsurface favorability': 1.0, 'surface favorability': 1.0, 'geothermal gradient': 74.0, 'Surface
Temperature': 25.0, 'Depth to Basement': 4.7, 'Production Temperature': 150.0, 'Derisking Time': 1, 'Production Well Count': 5, 'Discount Rate': 9.0, 'Mass Flow Rate / Well': 50.0, 'Linear 
Temperature Decline': 1.0, 'Sedimentary Drilling Cost Adjuster': 100.0, 'Basement Drilling Cost Multiplier': 1.2, 'Horizontal Well Length': 1000.0, 'Capital Expenditure Subsidy': 0.0, 'Annual
Fixed OPEX as % of Total CAPEX': 2.0, 'Wells and Plant Construction Time': 2, 'Power Plant Efficiency Increase Over Baseline': 0.0, 'Power Plant Cost': 2500.0}, '2_fixed_inputs': {'Injection 
Well Count': 5, 'Productivity Index': 5.0, 'Injectivity Index': 5.0, 'Well Diameter': 0.16256, 'Vertical Stimulation Cost': 0.6, 'Power Plant Type': 'ORC', 'Surface Temperature Losses': 4, 
'Air Cooler Losses': 2, 'Production Lifetime': 25, 'Capacity Factor': 90.0, 'Pump Efficiency': 80, 'Water Loss': 1, 'Injection Temperature': 75.0}, '3_outputs': {'Target Depth': 
1754.0208145214217, 'Maximum Net Output per well': 1.1981682831685536, 'Maximum Gross Plant Output': 7.0836794857457255, 'Maximum Annual Net Plant Output': 47231.793722504386, 'Inj. Pumping 
power': 0.42323277340802906, 'Average Net Electricity Sales': 3.9132001700991843, 'Cost per Well': 6.150792613168973, 'Derisking Cost': 10.263707442783698, 'Drilling Cost per Meter': 
1230.3194673409641, 'Stimulation Cost per Well': 2.7624671916010497, 'Surface Facility Cost': 17.709198714364312, 'Overnight Capital Costs': 22955.846431551057, 'Derisk + Total CAPEX 
(Subsidized)': 79.56711471793265, 'Total Dev. CAPEX': 79.56711471793265, 'Average OPEX': 4.642238356543925, 'Total OPEX': 39.78355735896633, 'Simple Pre-TAX Payout Price (Discounted)': 
292.21075195272374, 'Simple Pre-TAX Payout Price (0% Discount)': 151.24360014786419, 'Lifetime Project Generation': 856.9908372517212, 'Foregone CO2 Emissions vs Nat Gas Power': 
379.3958425871977, 'Surface Footprint': 0.651313036311308, 'Emissions Intensity of Nat Gas Power': 442.707}, '4_output_timeseries': {'WHT': [0.0, 0.0, 0.0, 150.0, 149.07208260135758, 
148.11365407812173, 147.1439909878709, 146.16854501530997, 145.18959739167167, 144.20831167525375, 143.225358781262, 142.24115954457648, 141.2559947147265, 140.27006062962118, 
139.2834998105236, 138.29641888848255, 137.30889966289487, 136.32100621826157, 135.3327896694109, 134.34429142218778, 133.35554547295487, 132.36658006722155, 131.37741891998758, 
130.38808212951972, 129.3985868723894, 128.4089479395925, 127.41917815536999, 126.42928870822743], 'Exergy': [0.0, 0.0, 0.0, 11.051367449702187, 10.901645199963262, 10.74793547583032, 
10.593392628788767, 10.438912358236244, 10.284871311325807, 10.131462831755147, 9.978799344833376, 9.826952149321873, 9.675969487800787, 9.525885706137187, 9.376726294369638, 
9.228510844774044, 9.081254879551528, 8.934971029033228, 8.789669818715087, 8.645360211171383, 8.502049989097722, 8.35974603232345, 8.218454522243162, 8.078181095435244, 7.938930960996055, 
7.800708991495247, 7.663519794448948, 7.527367769203265], 'Util. Eff.': [0.0, 0.0, 0.0, 0.32304768337218526, 0.32085959382063123, 0.318577067302107, 0.31624439095528245, 0.3138739195690349, 
0.3114707111060705, 0.30903725952065464, 0.3065749321425454, 0.3040845261516779, 0.3015665203275293, 0.2990212021745381, 0.29644873762074364, 0.2938492117616447, 0.29122265393633484, 
0.28856905382786746, 0.2858883721734983, 0.2831805481070726, 0.2804455043249029, 0.27768315080314654, 0.27489338752642556, 0.2720761065261387, 0.2692311934271665, 0.2663585286380744, 
0.26345798827867284, 0.2605294449113974], 'Gross plant output': [0.0, 0.0, 0.0, 7083.679485745725, 6940.381210257536, 6793.847793900145, 6647.129700040306, 6501.098325797209, 
6356.123525967169, 6212.397722708924, 6070.034829758292, 5929.1087296839205, 5789.670759130133, 5651.758576326467, 5515.401042744859, 5380.6210876624755, 5247.43747724629, 5115.865953751272, 
4985.919995061961, 4857.611336133479, 4730.950335972408, 4605.946241426313, 4482.607380251976, 4360.941304603533, 4240.9548990564945, 4122.654462796358, 4006.04577267804, 3891.1341319136236],
'Tot. pumping power': [0.0, 0.0, 0.0, 951.1644801880424, 951.7685938419324, 952.4484532970785, 953.1999674744071, 954.0210103444678, 954.9098228577775, 955.8647820345283, 956.8843332578731, 
957.9669648829952, 959.1111970372572, 960.3155760389488, 961.5786713169838, 962.899073544859, 964.275393404511, 965.7062606939528, 967.1903236298339, 968.7262482634966, 970.3127179640734, 
971.9484329411828, 973.6321097904721, 975.3624810515837, 977.138294771736, 978.9583140706717, 980.8213167039488, 982.7260946226651], 'Parasitic loss': [0.0, 0.0, 0.0, 15.427548241024203, 
15.71349159373647, 16.01927865019635, 16.340023596479956, 16.67476667071451, 17.023462318764096, 17.386406741803427, 17.764066600849738, 18.157014629989156, 18.565902223796893, 
18.991447229565406, 19.434428863189854, 19.895686350275504, 20.376119726738242, 20.876692028762733, 21.398432477611674, 21.94244045540653, 22.50989017124471, 23.102035976872404, 
23.72021832828336, 24.365870414764892, 25.0405254955464, 25.74582499952357, 26.48352745725793, 27.255518347792584], 'Net plant output': [0.0, 0.0, 0.0, 47231.793722504386, 46119.86255858721, 
44982.3384811724, 43842.82260044719, 42708.0643710177, 41580.93527773995, 40462.934831360195, 39354.955422453015, 38257.579809193936, 37171.2163022406, 36096.167321951645, 35032.66713991736, 
33980.90402620067, 32941.03392759639, 31913.1892764764, 30897.484864749527, 29894.02187728541, 28902.89073140158, 27924.173118748666, 26957.943500600366, 26004.270219973685, 
25063.21633969781, 24134.840280859575, 23219.196313463864, 22316.334936081777], 'Year Count': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 
27, 28], 'Revenue': [0.0, 0.0, 0.0, 13.801637959728943, 13.476719718201037, 13.144322952175328, 12.811344159806543, 12.479755604300413, 12.150396364405928, 11.82370461328582, 
11.499941117060919, 11.179276163935901, 10.86182906667507, 10.547688195758825, 10.236922007864727, 9.929585517529436, 9.625724294083126, 9.325377035688769, 9.02857728577636, 
8.735354611652744, 8.445735434230267, 8.159743624687582, 7.8774009414094746, 7.598727354960332, 7.32374129297689, 7.052459826728863, 6.784898814495187, 6.5210730125012955], 'Ops. & Maint.': 
[0.0, 0.0, 0.0, 1.591342294358653, 1.591342294358653, 1.591342294358653, 1.591342294358653, 1.591342294358653, 1.591342294358653, 1.591342294358653, 1.591342294358653, 1.591342294358653, 
1.591342294358653, 1.591342294358653, 1.591342294358653, 1.591342294358653, 1.591342294358653, 1.591342294358653, 1.591342294358653, 1.591342294358653, 1.591342294358653, 1.591342294358653, 
1.591342294358653, 1.591342294358653, 1.591342294358653, 1.591342294358653, 1.591342294358653, 1.591342294358653], 'D&CE': [0.0, 30.92895800178417, 30.92895800178417, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Derisking': [10.263707442783698, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Facility Capex': [0.0, 8.854599357182156, 8.854599357182156, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Capex. Subsid.': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Cash Flow': [-10.263707442783698, -39.78355735896633, -39.78355735896633, 12.210295665370289, 11.885377423842385, 11.552980657816676, 
11.220001865447891, 10.888413309941761, 10.559054070047274, 10.232362318927166, 9.908598822702267, 9.587933869577249, 9.270486772316417, 8.956345901400173, 8.645579713506073, 
8.338243223170782, 8.034381999724474, 7.734034741330116, 7.437234991417706, 7.144012317294091, 6.854393139871614, 6.568401330328929, 6.2860586470508215, 6.0073850606016785, 5.732398998618237,
5.46111753237021, 5.193556520136534, 4.929730718142642]}}

⠋ thinking...[2024-08-23 11:05:40,724] {_client.py:1026} INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
thought: I have completed the geo power analysis and gathered the results.
tool: final_answer
tool_input: The geo power analysis has been completed. Here are the key results:

- Target Depth: 1754.02 m
- Maximum Gross Plant Output: 7.08 MW
- Maximum Annual Net Plant Output: 47,231.79 MWh
- Total Development CAPEX: $79.57 million
- Average OPEX: $4.64 million per year
- Simple Pre-TAX Payout Price (Discounted): $292.21/MWh
- Production Lifetime: 25 years
- Foregone CO2 Emissions vs Natural Gas Power: 379.40 tons

If you need more specific details or further analysis, please let me know!

The geo power analysis has been completed. Here are the key results:

- Target Depth: 1754.02 m
- Maximum Gross Plant Output: 7.08 MW
- Maximum Annual Net Plant Output: 47,231.79 MWh
- Total Development CAPEX: $79.57 million
- Average OPEX: $4.64 million per year
- Simple Pre-TAX Payout Price (Discounted): $292.21/MWh
- Production Lifetime: 25 years
- Foregone CO2 Emissions vs Natural Gas Power: 379.40 tons

If you need more specific details or further analysis, please let me know!
>>> I'm not familiar with geothermal analysis, what are common things one might adjust to do an anlysis?
⠙ thinking...[2024-08-23 11:10:35,060] {_client.py:1026} INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
thought: I need to provide information on common parameters that can be adjusted in geothermal analysis.
tool: final_answer
tool_input: In geothermal analysis, there are several common parameters that can be adjusted to evaluate different scenarios. Here are some key ones:

1. **Subsurface Favorability (subsurf_fav)**: This indicates the potential for geothermal resources based on geological conditions.
2. **Geothermal Gradient**: The rate at which temperature increases with depth, affecting the efficiency of geothermal systems.
3. **Surface Temperature**: The temperature of the ground surface, which influences the required drilling depth.
4. **Depth to Basement**: The depth at which the crystalline basement rock is found, impacting drilling costs.
5. **Production Temperature**: The temperature of the geothermal fluid at the wellhead, which affects energy production.
6. **Number of Production Wells**: The quantity of wells that will extract heat, influencing the overall energy output.
7. **Flow Rate**: The amount of geothermal fluid that can be extracted per well, affecting the energy generation capacity.
8. **Drilling Cost Adjusters**: Factors that modify the estimated costs of drilling in sedimentary or basement rock.
9. **Power Plant Efficiency**: Adjustments to the efficiency of the power conversion process, which can vary with technology improvements.
10. **Discount Rate**: The rate used to calculate the present value of future cash flows, impacting economic viability assessments.

These parameters can be modified to explore different scenarios and optimize the design and economic feasibility of geothermal projects.

In geothermal analysis, there are several common parameters that can be adjusted to evaluate different scenarios. Here are some key ones:

1. **Subsurface Favorability (subsurf_fav)**: This indicates the potential for geothermal resources based on geological conditions.
2. **Geothermal Gradient**: The rate at which temperature increases with depth, affecting the efficiency of geothermal systems.
3. **Surface Temperature**: The temperature of the ground surface, which influences the required drilling depth.
4. **Depth to Basement**: The depth at which the crystalline basement rock is found, impacting drilling costs.
5. **Production Temperature**: The temperature of the geothermal fluid at the wellhead, which affects energy production.
6. **Number of Production Wells**: The quantity of wells that will extract heat, influencing the overall energy output.
7. **Flow Rate**: The amount of geothermal fluid that can be extracted per well, affecting the energy generation capacity.
8. **Drilling Cost Adjusters**: Factors that modify the estimated costs of drilling in sedimentary or basement rock.
9. **Power Plant Efficiency**: Adjustments to the efficiency of the power conversion process, which can vary with technology improvements.
10. **Discount Rate**: The rate used to calculate the present value of future cash flows, impacting economic viability assessments.

These parameters can be modified to explore different scenarios and optimize the design and economic feasibility of geothermal projects.
>>> can you do another analysis, but decrease the subsurface favorability, and increase the number of production wells
⠙ thinking...[2024-08-23 11:11:58,645] {_client.py:1026} INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
thought: I need to perform a new geo power analysis with decreased subsurface favorability and increased number of production wells.
tool: geo_power_api
tool_input: {'data': {'subsurf_fav': 0.5, 'no_of_prod_wells': 10}}

observation: {'1_user_inputs': {'application': 'power', 'latitude': None, 'Longitude': None, 'subsurface favorability': 0.5, 'surface favorability': 1.0, 'geothermal gradient': 74.0, 'Surface
Temperature': 25.0, 'Depth to Basement': 4.7, 'Production Temperature': 150.0, 'Derisking Time': 1, 'Production Well Count': 10, 'Discount Rate': 9.0, 'Mass Flow Rate / Well': 50.0, 'Linear 
Temperature Decline': 1.0, 'Sedimentary Drilling Cost Adjuster': 100.0, 'Basement Drilling Cost Multiplier': 1.2, 'Horizontal Well Length': 1000.0, 'Capital Expenditure Subsidy': 0.0, 'Annual
Fixed OPEX as % of Total CAPEX': 2.0, 'Wells and Plant Construction Time': 2, 'Power Plant Efficiency Increase Over Baseline': 0.0, 'Power Plant Cost': 2500.0}, '2_fixed_inputs': {'Injection 
Well Count': 5, 'Productivity Index': 5.0, 'Injectivity Index': 5.0, 'Well Diameter': 0.16256, 'Vertical Stimulation Cost': 0.6, 'Power Plant Type': 'ORC', 'Surface Temperature Losses': 4, 
'Air Cooler Losses': 2, 'Production Lifetime': 25, 'Capacity Factor': 90.0, 'Pump Efficiency': 80, 'Water Loss': 1, 'Injection Temperature': 75.0}, '3_outputs': {'Target Depth': 
1754.0208145214217, 'Maximum Net Output per well': 1.1340453298281277, 'Maximum Gross Plant Output': 14.167358971491451, 'Maximum Annual Net Plant Output': 89408.13380364959, 'Inj. Pumping 
power': 0.7438475401101592, 'Average Net Electricity Sales': 7.249293760134534, 'Cost per Well': 6.150792613168973, 'Derisking Cost': 10.263707442783698, 'Drilling Cost per Meter': 
1230.3194673409641, 'Stimulation Cost per Well': 2.7624671916010497, 'Surface Facility Cost': 35.418397428728625, 'Overnight Capital Costs': 19102.750165299374, 'Derisk + Total CAPEX 
(Subsidized)': 128.21774013193, 'Total Dev. CAPEX': 128.21774013193, 'Average OPEX': 4.038111520891423, 'Total OPEX': 64.108870065965, 'Simple Pre-TAX Payout Price (Discounted)': 
241.40642404208472, 'Simple Pre-TAX Payout Price (0% Discount)': 127.60828491347758, 'Lifetime Project Generation': 1587.595333469463, 'Foregone CO2 Emissions vs Nat Gas Power': 
702.8395672942655, 'Surface Footprint': 1.206572453436792, 'Emissions Intensity of Nat Gas Power': 442.707}, '4_output_timeseries': {'WHT': [0.0, 0.0, 0.0, 150.0, 149.07208260135758, 
148.11365407812173, 147.1439909878709, 146.16854501530997, 145.18959739167167, 144.20831167525375, 143.225358781262, 142.24115954457648, 141.2559947147265, 140.27006062962118, 
139.2834998105236, 138.29641888848255, 137.30889966289487, 136.32100621826157, 135.3327896694109, 134.34429142218778, 133.35554547295487, 132.36658006722155, 131.37741891998758, 
130.38808212951972, 129.3985868723894, 128.4089479395925, 127.41917815536999, 126.42928870822743], 'Exergy': [0.0, 0.0, 0.0, 11.051367449702187, 10.901645199963262, 10.74793547583032, 
10.593392628788767, 10.438912358236244, 10.284871311325807, 10.131462831755147, 9.978799344833376, 9.826952149321873, 9.675969487800787, 9.525885706137187, 9.376726294369638, 
9.228510844774044, 9.081254879551528, 8.934971029033228, 8.789669818715087, 8.645360211171383, 8.502049989097722, 8.35974603232345, 8.218454522243162, 8.078181095435244, 7.938930960996055, 
7.800708991495247, 7.663519794448948, 7.527367769203265], 'Util. Eff.': [0.0, 0.0, 0.0, 0.32304768337218526, 0.32085959382063123, 0.318577067302107, 0.31624439095528245, 0.3138739195690349, 
0.3114707111060705, 0.30903725952065464, 0.3065749321425454, 0.3040845261516779, 0.3015665203275293, 0.2990212021745381, 0.29644873762074364, 0.2938492117616447, 0.29122265393633484, 
0.28856905382786746, 0.2858883721734983, 0.2831805481070726, 0.2804455043249029, 0.27768315080314654, 0.27489338752642556, 0.2720761065261387, 0.2692311934271665, 0.2663585286380744, 
0.26345798827867284, 0.2605294449113974], 'Gross plant output': [0.0, 0.0, 0.0, 14167.35897149145, 13880.762420515071, 13587.69558780029, 13294.259400080611, 13002.196651594419, 
12712.247051934339, 12424.795445417849, 12140.069659516585, 11858.217459367841, 11579.341518260266, 11303.517152652934, 11030.802085489719, 10761.242175324951, 10494.87495449258, 
10231.731907502544, 9971.839990123923, 9715.222672266958, 9461.900671944815, 9211.892482852625, 8965.214760503952, 8721.882609207067, 8481.909798112989, 8245.308925592715, 8012.09154535608, 
7782.268263827247], 'Tot. pumping power': [0.0, 0.0, 0.0, 1799.7109536701857, 1800.9191809779659, 1802.2788998882581, 1803.7819282429155, 1805.4240139830367, 1807.2016390096562, 
1809.1115573631578, 1811.1506598098474, 1813.3159230600913, 1815.6043873686156, 1818.0131453719987, 1820.5393359280688, 1823.1801403838192, 1825.9327801031232, 1828.7945146820068, 
1831.7626405537687, 1834.8344898210944, 1838.0074292222478, 1841.2788591764665, 1844.6462128750452, 1848.1069553972684, 1851.658582837573, 1855.2986214354444, 1859.0246267019986, 
1862.834182539431], 'Parasitic loss': [0.0, 0.0, 0.0, 19.95365317486958, 20.333047162647837, 20.73847131432983, 21.163380160444493, 21.606468217665263, 22.067649477687258, 22.54729277989703, 
23.045992910898555, 23.564484476122708, 24.103605144062794, 24.66427918748183, 25.24751052701333, 25.85438073664078, 26.486049918233924, 27.143759414823453, 27.828835833856182, 
28.54269610610522, 29.286853443598115, 30.062924139623647, 30.87263520321626, 31.717832853784188, 32.6004919260656, 33.52272625562982, 34.486800132991725, 35.495140931670335], 'Net plant 
output': [0.0, 0.0, 0.0, 89408.13380364959, 87184.27147581523, 84909.22332098562, 82630.19155953519, 80360.67510067624, 78106.41691412071, 75870.4160213612, 73654.45720354684, 
71459.70597702869, 69286.978963122, 67136.88100254408, 65009.88063847554, 62906.35441104216, 60826.614213833585, 58770.924911593625, 56739.51608813987, 54732.590113211634, 52750.32782144396, 
50792.89259613814, 48860.43335984154, 46953.08679858819, 45070.979038036436, 43214.226920359964, 41382.93898556854, 39577.21623080437], 'Year Count': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28], 'Revenue': [0.0, 0.0, 0.0, 21.58369786181528, 21.046843209690884, 20.497631970109925, 19.94745906229984, 19.399583209662048, 
18.855390801978082, 18.315605822302103, 17.78065912826901, 17.250832083013286, 16.726321824166433, 16.207274364163137, 15.69380281233714, 15.18599806789371, 14.68393542394901, 
14.187678820553693, 13.69728368071618, 13.212798857791583, 12.73426800642248, 12.261730568387389, 11.795222494545929, 11.334776781784791, 10.880423877648136, 10.43219198858729, 
9.990107316857879, 9.554194243818838], 'Ops. & Maint.': [0.0, 0.0, 0.0, 2.5643548026386, 2.5643548026386, 2.5643548026386, 2.5643548026386, 2.5643548026386, 2.5643548026386, 2.5643548026386, 
2.5643548026386, 2.5643548026386, 2.5643548026386, 2.5643548026386, 2.5643548026386, 2.5643548026386, 2.5643548026386, 2.5643548026386, 2.5643548026386, 2.5643548026386, 2.5643548026386, 
2.5643548026386, 2.5643548026386, 2.5643548026386, 2.5643548026386, 2.5643548026386, 2.5643548026386, 2.5643548026386], 'D&CE': [0.0, 46.39967135160068, 46.39967135160068, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Derisking': [10.263707442783698, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Facility Capex': [0.0, 17.709198714364312, 17.709198714364312, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Capex. Subsid.': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'Cash Flow': [-10.263707442783698, -64.108870065965, -64.108870065965, 19.01934305917668, 18.482488407052283, 17.933277167471324, 
17.383104259661238, 16.835228407023447, 16.29103599933948, 15.751251019663503, 15.21630432563041, 14.686477280374685, 14.161967021527833, 13.642919561524536, 13.129448009698539, 
12.62164326525511, 12.119580621310408, 11.623324017915092, 11.132928878077582, 10.648444055152982, 10.169913203783882, 9.697375765748788, 9.230867691907328, 8.770421979146192, 
8.316069075009537, 7.86783718594869, 7.425752514219279, 6.989839441180238]}}

⠸ thinking...[2024-08-23 11:12:02,447] {_client.py:1026} INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
thought: I have completed the geo power analysis with the adjusted parameters.
tool: final_answer
tool_input: The geo power analysis has been completed with decreased subsurface favorability and increased number of production wells. Here are the key results:

- Target Depth: 1754.02 m
- Maximum Gross Plant Output: 14.17 MW
- Maximum Annual Net Plant Output: 89,408.13 MWh
- Total Development CAPEX: $128.22 million
- Average OPEX: $4.04 million per year
- Simple Pre-TAX Payout Price (Discounted): $241.41/MWh
- Production Lifetime: 25 years
- Foregone CO2 Emissions vs Natural Gas Power: 702.84 tons

If you need more specific details or further analysis, please let me know!

The geo power analysis has been completed with decreased subsurface favorability and increased number of production wells. Here are the key results:

- Target Depth: 1754.02 m
- Maximum Gross Plant Output: 14.17 MW
- Maximum Annual Net Plant Output: 89,408.13 MWh
- Total Development CAPEX: $128.22 million
- Average OPEX: $4.04 million per year
- Simple Pre-TAX Payout Price (Discounted): $241.41/MWh
- Production Lifetime: 25 years
- Foregone CO2 Emissions vs Natural Gas Power: 702.84 tons

If you need more specific details or further analysis, please let me know!
>>> how is this different from the first one?
⠹ thinking...[2024-08-23 11:12:59,870] {_client.py:1026} INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
thought: I need to summarize the differences between the two geo power analyses.
tool: final_answer
tool_input: The second geo power analysis differs from the first in several key aspects due to the adjustments made:

1. **Subsurface Favorability**: 
   - First Analysis: 1.0 (higher favorability)
   - Second Analysis: 0.5 (lower favorability)

2. **Number of Production Wells**: 
   - First Analysis: 5 wells
   - Second Analysis: 10 wells

3. **Maximum Gross Plant Output**: 
   - First Analysis: 7.08 MW
   - Second Analysis: 14.17 MW (increased due to more wells)

4. **Maximum Annual Net Plant Output**: 
   - First Analysis: 47,231.79 MWh
   - Second Analysis: 89,408.13 MWh (significantly increased due to more wells)

5. **Total Development CAPEX**: 
   - First Analysis: $79.57 million
   - Second Analysis: $128.22 million (higher due to more wells and lower favorability)

6. **Average OPEX**: 
   - First Analysis: $4.64 million per year
   - Second Analysis: $4.04 million per year (slightly lower despite more wells)

7. **Simple Pre-TAX Payout Price (Discounted)**: 
   - First Analysis: $292.21/MWh
   - Second Analysis: $241.41/MWh (lower due to increased output)

8. **Foregone CO2 Emissions vs Natural Gas Power**: 
   - First Analysis: 379.40 tons
   - Second Analysis: 702.84 tons (higher due to increased output)

Overall, the second analysis shows a higher output and capacity due to the increased number of production wells, but the lower subsurface favorability impacts the overall economic viability 
reflected in the CAPEX and payout price.

The second geo power analysis differs from the first in several key aspects due to the adjustments made:

1. **Subsurface Favorability**: 
   - First Analysis: 1.0 (higher favorability)
   - Second Analysis: 0.5 (lower favorability)

2. **Number of Production Wells**: 
   - First Analysis: 5 wells
   - Second Analysis: 10 wells

3. **Maximum Gross Plant Output**: 
   - First Analysis: 7.08 MW
   - Second Analysis: 14.17 MW (increased due to more wells)

4. **Maximum Annual Net Plant Output**: 
   - First Analysis: 47,231.79 MWh
   - Second Analysis: 89,408.13 MWh (significantly increased due to more wells)

5. **Total Development CAPEX**: 
   - First Analysis: $79.57 million
   - Second Analysis: $128.22 million (higher due to more wells and lower favorability)

6. **Average OPEX**: 
   - First Analysis: $4.64 million per year
   - Second Analysis: $4.04 million per year (slightly lower despite more wells)

7. **Simple Pre-TAX Payout Price (Discounted)**: 
   - First Analysis: $292.21/MWh
   - Second Analysis: $241.41/MWh (lower due to increased output)

8. **Foregone CO2 Emissions vs Natural Gas Power**: 
   - First Analysis: 379.40 tons
   - Second Analysis: 702.84 tons (higher due to increased output)

Overall, the second analysis shows a higher output and capacity due to the increased number of production wells, but the lower subsurface favorability impacts the overall economic viability reflected in the CAPEX and payout price.
>>>