Dawson Geophysical (DWSN) Operating Expenses (2010 - 2026)
Dawson Geophysical (DWSN) posted Operating Expenses of $20.94 million for Q2 2026, up 71.3% from $12.22 million a year earlier but down 26.7% from the prior quarter.
Dawson Geophysical (DWSN) Operating Expenses (2010 - 2026) Analysis & Trends
For the trailing twelve months through Jun 30, 2026, Operating Expenses at Dawson Geophysical was $99.52 million, up 55.7% year-over-year; for FY2025, it came in at $77.29 million, down 1.8% from FY2024.
- Annual Operating Expenses shows a five-year compound annual growth rate of -5.0% (FY2020 to FY2025).
- In prior years, Dawson Geophysical's Operating Expenses was $78.7 million in FY2024 (-28.4%), $109.92 million in FY2023 (+48.9%), $73.83 million in FY2022 (+36.9%) and $53.93 million in FY2021 (-46.1%).
- Quarterly Operating Expenses has run from a low of $1.75 million in Q1 2023 to a high of $28.65 million in Q3 2023 over five years.
- On a year-over-year basis, Operating Expenses has increased in each of the last four quarters, with growth averaging 13.1% over the last eight quarters.
- The strongest year-over-year quarter for Operating Expenses in the past five years was Q2 2023, with growth of 98.0%; the weakest was Q1 2023, with a decline of 92.5%.
- According to Business Quant data, Operating Expenses for the three prior quarters was $28.56 million (Q1 2026), $26.11 million (Q4 2025) and $23.92 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Slb | 76.39 Bn | 73.50 Bn | 1.40 Bn | 324.00 Mn |
| 2 | Baker Hughes | 56.70 Bn | 19.98 Bn | 1.58 Bn | 723.00 Mn |
| 3 | TechnipFMC | 27.74 Bn | 23.88 Bn | 684.70 Mn | 2.28 Bn |
| 4 | Halliburton | 27.03 Bn | 18.79 Bn | 804.00 Mn | 4.94 Bn |
| 5 | Nov | 6.97 Bn | 1.76 Bn | 521.00 Mn | 1.04 Bn |
| 6 | Noble | 6.81 Bn | 4.74 Bn | 239.27 Mn | 689.21 Mn |
| 7 | Weatherford International | 5.95 Bn | 1.88 Bn | 333.00 Mn | 998.00 Mn |
| 8 | Transocean | 5.90 Bn | 3.61 Bn | 358.00 Mn | 812.00 Mn |
| 9 | Valaris | 5.52 Bn | 3.13 Bn | 123.70 Mn | 498.70 Mn |
| 10 | Dawson Geophysical | 90.06 Mn | 71.83 Mn | 2.61 Mn | 20.94 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 20.94 Mn |
| Mar 31, 2026 | 28.56 Mn |
| Dec 31, 2025 | 26.11 Mn |
| Sep 30, 2025 | 23.92 Mn |
| Jun 30, 2025 | 12.22 Mn |
| Mar 31, 2025 | 15.04 Mn |
| Dec 31, 2024 | 16.47 Mn |
| Sep 30, 2024 | 20.17 Mn |
| Jun 30, 2024 | 16.28 Mn |
| Mar 31, 2024 | 25.78 Mn |
| Dec 31, 2023 | 26.29 Mn |
| Sep 30, 2023 | 28.65 Mn |
| Jun 30, 2023 | 25.00 Mn |
| Mar 24, 2023 | 1.75 Mn |
| Dec 31, 2022 | 23.35 Mn |
| Sep 30, 2022 | 14.49 Mn |
| Jun 30, 2022 | 12.62 Mn |
| Mar 31, 2022 | 23.37 Mn |
| Dec 31, 2021 | 17.60 Mn |
| Sep 30, 2021 | 9.67 Mn |
Dawson Geophysical Operating Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=operating-expenses&ticker=DWSN&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "DWSN", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()
const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=DWSN&period=max&api_key=YOUR_API_KEY");
const data = await res.json();