What the data for this category looks like
Oil and gas exploration investment research data sources include drilling operation logs, core analysis reports, block production ledgers, reservoir simulation files, and industry development specification documents. Data update frequency follows multiple tiers: drilling operation data updates in real time alongside project progress; single-well production daily reports update daily; block development plans are updated quarterly; annual reserve assessment files are updated per project cycle.
Document structures include structured tables with fields such as well ID, daily oil production, formation pressure, geological reports with visual charts, and plain-text industry standards. Field units mostly use common engineering units like cubic meters, megapascals, and meters. No percentage-based statistical values are used.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Oil and gas exploration data characteristics impose multiple constraints on multi-turn dialogue and prompt engineering. Real-time updated operation data requires dialogue contexts to prioritize recalling the latest entries, and limits the proportion of non-real-time documents in context. Mixed structured and unstructured document types require prompts to clearly distinguish processing logic for field extraction and text descriptions, to avoid confusion between parameters and qualitative descriptions. The multi-unit field system requires prompts to include built-in unit conversion rules, to prevent incorrect output units. Long documents such as reservoir simulation files require segmented processing, and appropriate context window configuration is needed to support long-text recall. Multi-well comparison questions require retaining well IDs and block information from historical conversations, to avoid repeated input of core parameters.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
max_history_tokens | 8000–12000 token | Multi-turn dialogue for oil and gas exploration investment research often involves multiple well parameters and block information. 8000–12000 tokens can cover the historical data of 3 to 5 core rounds of dialogue, avoiding context overflow |
recall count | Top 6–8 entries | The oil and gas exploration knowledge base contains multiple types of documents. 6 to 8 recall entries can cover core information of structured production data and unstructured geological descriptions, avoiding redundancy |
similarity threshold | 0.72–0.80 | Professional terminology in oil and gas exploration has high similarity. 0.72–0.80 can filter low-relevance results while retaining valid content matched by professional terminology |
segment length | 1200–1500 characters | Reservoir simulation files and geological reports are mostly long texts. Segmentation of 1200–1500 characters balances recall accuracy and token usage efficiency |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large reservoir simulation files have large file sizes. 600 seconds ensures complete parsing and avoids timeout interruptions |
system_prompt_template | Output in oil and gas exploration professional terminology, clearly mark field units, and prioritize recalling the latest production data | Matches the professional scenario of oil and gas exploration investment research, unifying output format and information priority |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: Occasional empty AI dialogue responses, returning empty strings or content-free JSON fields. Cause: No reasonable
max_history_tokensparameter set. Cumulative historical data from multi-turn dialogue exceeds the token limit supported by the model, leading to model call failure. - Phenomenon: Unclosed quotation mark garbled characters appear at the end of AI output content, followed by automatic correction to normal quotation marks. Cause: The prompt does not clearly specify output format constraints, and the processing rules for quotation symbols are not unified during long-document segmented parsing, leading to format confusion.
- Phenomenon: Historical conversation files associated with the knowledge base cannot be loaded, showing file expired. Cause: The default configuration of
CHAT_FILE_EXPIRE_TIMEwas not modified, retaining the 7-day expiration duration, causing historical conversation files to be cleaned up prematurely.
How to confirm correct configuration
- Initiate 3 consecutive dialogue rounds containing multiple well parameters and block information, check whether the dialogue context fully retains the core parameters of historical input, and adjust
max_history_tokensto cover all valid historical content. - Upload a single large reservoir simulation file, verify that the parsing process does not trigger a timeout error, and adjust
PARSE_FILE_TIMEOUT_SECONDSto match the file parsing duration. - Trigger more than 5 AI dialogue requests, check whether the output content uniformly marks professional field units, and adjust
system_prompt_templateto clarify output format requirements. - Retrieve investment research questions containing professional terminology, check whether the matching degree of recall results meets expectations, and adjust the
similarity thresholdto filter invalid low-matching content.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.