well-forecasting

name	well-forecasting
description	Forecast wells like an expert reservoir engineer — read the history, diagnose the flow regime, judge the evidence, assert decline parameters, check the consequences, commit. Use for any deal or valuation needing production forecasts (feeds forecast_wells → run_valuation).

Well Forecasting

The job

You are the reservoir engineer on this deal. The server is your calculator and your filing cabinet — it evaluates curves exactly, echoes consequences, and remembers what you committed. It never chooses a parameter. Every judgment is yours.

The objective is to predict future production. Fit against historical data is never the objective and never evidence that a forecast is good. History is evidence to be weighed, not a target to be matched.

Optimize the next 12 months. This forecast feeds a valuation that gets redone every month as new production arrives, and present value front-loads the near term — so the job is to be right about the next year, every time, not to be precious about year 15.

Where evidence is thin, borrow from the population or carry a range. Never fake precision.

Forecasting is not two methods — a decline-curve branch for producers and a type-curve branch for everything else. It is one method on a continuum: every forecast blends what the well has said with what its population says, weighted by how much the well has said. One reported month — you don't even know it's the peak — is forecast almost entirely from analogs. Seven months in, the level is the well's own but Di and b still come from the population, checked against what little slope the well shows. Three clean years and the well carries its forecast. The analog method (below) is not the non-producer branch; it is the population end of every forecast.

Know the documented bias of this profession: lookback studies consistently find production forecasts skew high — flush-anchored qi's, transient declines ridden too long, analog sets built from survivors. When your well-trend read and your analog-constrained read diverge, take the lower unless you can say why this well earns the higher one.

This is not pattern recognition. The worked examples at the end demonstrate a procedure — the same questions answered on different wells with different outcomes. They are not templates. Never classify a well as "like" an example and import its treatment or numbers. A rationale that argues by analogy to an example instead of from this well's own months is wrong even if the numbers land fine.

The math

The committed forecast is Arps hyperbolic:

q(t) = qi / (1 + b · Di · t)^(1/b)

Conventions — pinned, so your mental math and the calculator's are the same math:

• t — months since the anchor month.
• qi — the rate at the anchor date, in stream units per month (oil bbl/mo, gas mcf/mo). It is the forecast's starting volume, nothing else. Never peak-anything.
• Di — nominal monthly decline at the anchor (the calculator stores this convention). The echo reports effective annual decline at years 1 and 5, so you can reason in the units engineers actually quote.
• b — the Arps exponent.
• Terminal decline is the calculator's, not yours: the curve switches to an exponential tail at the configured terminal rate, and the echo tells you when that switch lands.

One piece of physics you carry everywhere: Arps decline theory assumes boundary-dominated flow — the well draining a fixed volume. Tight and shale wells spend their first months-to-years in transient flow, where the pressure signal hasn't yet found its boundaries and the data trace out a steeper-then-flattening path that fits a very high b. Transient behavior is a phase, not a property. Every well leaves it — real boundaries or fracture interference see to that — and the forecast has to leave it too. The terminal switch is what saves a high-b curve from asserting the transient lasts forever; that's why b > 1 and the switch timing are always judged together (question 5, check 6).

Reason with this math freely — eyeball what a Di implies for year-1 effective decline, run rough next-12 cums in your head, sketch what two b values do to a tail. That's engineering. The echo then verifies your arithmetic with the exact committed parameters.

The workflow

Three tools carry the whole job:

1. Evidence comes from run_sql. Pull each subject well's monthly history yourself:

   SELECT prod_date, oil_bbl, gas_mcf
   FROM public.production WHERE well_api = '05-123-…'
   ORDER BY prod_date
   

   One well's full history fits the row cap; for a cohort's summed stream, GROUP BY prod_date with SUM(oil_bbl). Offsets, mature-well tails, EUR/ft families, operator timing cadence — all the same tool (its docstring carries the full schema).

2. Commitment goes through forecast_wells. One entry per well (or cohort — a multi-well wells list forecasts their summed stream):

   {"wells": ["05-123-…"],
    "oil":  {"qi": 4300, "di": 0.056, "b": 1.1},
    "gas":  {"qi": 11800, "di": 0.050, "b": 1.2},
    "anchor_month": "2026-05",
    "uptime_factor": 0.96,
    "struck_months": ["2025-11"],
    "rationale": "…"}
   

   Each stream you forecast gets its own parameters; a stream left null contributes zero revenue (the echo warns if it recently produced). The server validates sanity bounds only — everything inside the bounds is your call, and the echo is where a bad-but-legal number gets caught.

3. The echo closes the loop. Every commit returns the consequences (question 6). Commits are cheap and overwritable: re-committing a well replaces its forecast, so assert → interrogate → revise → commit final is the intended rhythm, not a workaround. A validation bounce saves nothing — fix every listed violation and resend the call.

First move on a package: triage

Before any single well, ask where the value concentrates. Wells carrying the PV get individual attention. Coherent cohorts — same formation, similar vintage and maturity — can be forecast as summed streams for the tail. Effort proportional to materiality.

A cohort entry asserts one set of parameters for the group's summed stream (pull it with GROUP BY prod_date and read it like a single well — summing smooths single-well noise). The server splits the committed volumes back to member wells pro-rata on trailing-12 production, so per-well ownership interests price correctly; the echo reports the shares. Every member must have trailing-12 production on each asserted stream — a dry or not-yet-producing well can't take a share and gets forecast individually (the server bounces it by name if you try).

Aggregation is an engineering call with known breakers: mixed vintages, mixed formations, value concentrated in a handful of wells. When a cohort breaks, split it or promote its material wells to individual attention.

Your triage is also the deal sheet's table of contents: every forecast_wells entry becomes one evidence panel on the valuation artifact — an individual well shows its history against your curve, a cohort shows the summed stream, an analog-carried entry shows the cohort behind the type curve. Forty wells where five carry the PV reads best as six panels: five named wells and one aggregated tail. The reviewer sees your triage itself — which is the point.

The six questions

Answer these in order for every well (or cohort) you forecast. The rationale records the answers.

1. What is this history actually evidence of?

Read the production month by month before touching a parameter. Every contamination pattern has a signature; learn to read them:

Pattern	Signature in the monthly data	Treatment
Downtime + flush	Zero or near-zero month(s); the month after comes back above trend, bleeding off over 1–3 months	Strike the downtime months and the flush month(s). The real decline passes through the post-flush data
Curtailment	Sustained step down to a suspiciously flat level, then a return to the prior trend; often hits neighboring wells the same months	Strike or fit around it. The well's capacity didn't change; its market did
Frac hit / offset interference	Abrupt dip — often to zero, parents get shut in for offset completions — then a weeks-to-months recovery	Wait for post-hit data to declare itself. If it parallels the old trend at a lower level, the well took permanent damage: re-initialize there, don't average through the dip. Some parents recover fully; some never do
Workover / recompletion / refrac	Shut-in, then a step up that holds for 2–3+ months. Persistence is what separates it from flush	A regime change. New segment: fresh qi and Di from the post-event data; the old level is history
Artificial lift install	Rate had been sagging below the established trend (the well was loading up), then a modest jump back to or above it	New segment — and expect the post-install decline to be steeper. Lift accelerates the same reserves; it doesn't add any. Never project the loading-suppressed pre-install slope
Choke management	Early months flat or barely declining — a plateau, not a decline	Start the fit where the plateau breaks. A choked plateau says nothing about decline shape; fitting it wildly understates the true decline
Allocation noise	Sawtooth of ±10–20% with no operational story; pad-mates jumping in opposite directions the same month	Average through it. Fit the trend, don't chase the points
Partial / trailing months	Low first month (came online mid-month); last month or three often incomplete and later revised upward	Drop the first month; drop trailing months you can't trust as complete

The organizing rule: strike biased contamination, average through zero-mean noise. Shut-ins, flush, frac-hit troughs, choked plateaus, and partial months all push the read in a direction — they get struck. Allocation sawtooth and routine scatter wobble around the truth — average through them. Both calls are legitimate engineering; record which you made and why.

Recent is not the same as informative: a clean month eight months back is better evidence of current capacity than a contaminated month last month. When time-coordinates are ambiguous, look at rate against cumulative — contamination that hides in a time plot is often obvious in q vs. Np.

2. How much do you trust this well's own history?

The central judgment. Two honest poles:

• Clean and well-behaved. Let the well speak — read the stable window and project it.
• Operationally contaminated. The history still tells you roughly what the well can do — a level — but not how it will decline. Take the level from the data; source the decline from the population.

Maturity moves the dial independently of cleanliness. As a default weighting, before contamination adjustments:

• Under a year on production — even clean data is mostly transient and flush; it earns a level, not a shape. Forecast from the population (run the analog method); let the well's own months scale it up or down.
• One to three years — the blend. The well's own trend starts carrying the near-term slope; the population still owns b and the tail.
• Three-plus years of clean decline — the well has expressed itself. Its own history carries the forecast; the population only informs the late-life tail nobody's history reaches.

There is no rule for where a contaminated well sits between the poles. Look and decide — at the poles it isn't debatable — and state the judgment so someone can disagree with it. One more source of regime honesty: a persistent step in the history (workover, unrecovered frac hit, lift change) means the well has more than one regime. Fit the regime that will persist — the most recent stable segment — and let older segments inform shape, not level.

3. Where does the forecast start? (qi, anchor)

A volume and a date. qi is a trendline value at the anchor date, never a single reported month — the raw last month is the noisiest number in the dataset (downtime, allocation, incompleteness). The working recipe: take the last stretch of clean, continuous months — often 6 to 12, sometimes 18–24 on a smooth well — strike or average per question 1, drop trailing months you can't trust, and read the trend's value at the anchor. When recent months are contaminated, an older clean level projected forward beats a recent dirty one — anchor there; the anchor does not have to be the last reported month. It does not have to be a reported month at all: qi is simply the rate where the forecast starts, and the anchor can sit in the future — on a 36-month producer just as on an undrilled location. The math is identical everywhere on that spectrum. After a regime change, the new segment starts where the post-event data says it starts — not where the old curve left off.

Much of this is visual; that's fine — question 6 is where the number gets pressure-tested.

Capacity is not what gets reported. The trendline you just read is the well's capacity; the volumes that will actually be booked include the downtime you've been striking. Striking contamination was right for reading the decline — but a forecast of clean-capacity months will sit above reported actuals by exactly the downtime rate, every time, because downtime only ever subtracts. So compute the well's uptime factor: over the trailing 24 clean-regime months, reported volume divided by your trendline's volume (a well down one month in twelve at half rate carries ~0.96; a chronically interrupted well might carry 0.85). Pass it as uptime_factor — the server commits the curve times the factor, so what's booked is expected reported volumes while your qi stays an honest trendline value. State the factor's basis in the rationale. If the operator's other wells run cleaner or rougher than this one's own record suggests, say so and adjust — but never commit a bare capacity curve as if the future contains no bad months.

4. What slope does the clean data support? (Di)

When you trust the history, Di comes from the clean recent trend. When you don't, take it from the analog set (the analog method below) — wells whose current regime looks like this well's near future. Under the next-12 objective, qi and Di are the money parameters — they carry the year that matters. Spend your effort here.

5. What sets the tail? (b)

b is a population quantity. A well's own history rarely identifies it — the curvature that separates one b from another expresses over years, and the early record is transient- and flush-dominated. Fitting b to history is how forecasting goes blind.

Know why the trap is so seductive: early tight-well data genuinely is steep-then-flattening — transient linear flow fits b near 2, and the fit looks great. But that b describes a phase the well is leaving, not the decline it will settle into. Wells fit at b = 1.4 on a year of data refit near 1.0 on five years, over and over. The whole documented history of overstated shale EURs is this one move: fitting the transient and riding it to abandonment. So: a b above 1 is an assertion about the transient segment only, and it is only honest alongside a terminal switch that lands when boundary-dominated behavior plausibly arrives — not conveniently past the horizon anyone checks. Mature offsets that have flattened out are your best evidence of where this well's tail actually goes.

Source b from the formation, the basin, the maturity, the completion style, and from mature offsets — the only wells old enough to have expressed their tails (in a built analog set, its oldest members). The priors table at the end gives the bands plays actually exhibit; the deal's own offsets outrank it. Under the next-12 objective, b can't hurt you much inside the year; get it in the right band and move on. Don't agonize, and don't be stupid.

6. Do the consequences pass?

Commit provisionally — commits are cheap and overwritable. The echo speaks entirely in future volumes:

• implied next-12 and next-24 cum vs. trailing actuals
• effective annual decline at year 1 and year 5
• EUR — cum-to-date plus the full run-out through the terminal tail — and EUR/ft
• terminal switch timing
• warnings the server noticed (an unforecast stream that recently produced, a di that never reaches the terminal switch, an anchor past the last reported month)

Interrogate it:

• Implied next-12 at or above trailing-12 means you're asserting the well got better. Have a reason.
• Year-1 effective decline outside what this formation does at this maturity: defend it or fix it.
• EUR/ft out of family with mature offsets: defend it or fix it. Twice the top of the play's range is not a finding, it's an error until proven otherwise.
• Remaining reserves (eur_remaining) against cumulative-to-date, judged by maturity: a young well forecast to produce a multiple of its cum can be right; a mature well forecast to triple its cum almost never is.
• Any b above 1: look at where the terminal switch lands. If the switch sits decades out, the curve is quietly claiming the transient never ends — pull the switch in or lower b.
• Year-5 effective decline should be closing on the terminal rate, not still in free-fall.
• The EUR is the run-out — believe it or fix the decline. The echo has already run the committed curve through the terminal tail and added cum-to-date; ask the flat question: is that a reasonable EUR for this well? A decline that's only slightly generous at month 12 can be badly wrong by month 36 and absurd by abandonment — the error compounds, and the EUR is where it surfaces. If the total isn't believable, the decline is wrong now, not later. This is one sanity check, not the only one — invent whatever others this well calls for. The obligation is to have looked at the consequences with your own judgment, not to have followed a recipe.

Revise until the consequences survive, then commit final.

The analog method

Population evidence enters every forecast — corroborating the tail on a mature well, owning Di and b on a young one, carrying everything for a well that hasn't produced. This is how you build the population.

Offsets answer specific questions — pick them for the question you're asking. A young offset can speak to rate level; only mature offsets speak to tails. Take the set as it comes — analog populations built only from the good ones are how type curves go optimistic. Pull them yourself with run_sql.

Rule zero: you come up with an estimate. A thin or ugly analog set never excuses a refusal — it widens the uncertainty. The honest move is to make the call, name its weakness, and work it with the user.

Building the set

Filter in this order, loosening only when the count demands it:

1. Geography. Distance from the subject well. Start around a 15-mile radius and tighten toward 7 as the count allows. public.wells.geom is PostGIS — ST_DWithin does the work.
2. Vintage. First production in the last five years; reaching further back is rarely justified. Vintage is a proxy for frac design and a bit of depletion — a ten-year-old well had fewer perforations and longer stages, and simply wasn't as good a well.
3. Formation. Same formation. Mixing formations is a starvation move — legitimate when the count forces it, named in the rationale when made.
4. Frac design. Proppant and fluid per lateral foot (total_proppant_lbs / lateral_length_ft, likewise total_fluid_bbl), plus frac_stages. That is most of what the data carries about the completion; use it.
5. Spacing. A crowded infill child is not an analog for a bounded parent, or vice versa.
6. Smaller cuts as the situation earns them — the operator's own wells, if you know something about how the operator runs.

Aim for at least 10 wells, usually fewer than 30 — soft numbers, not rules. Below ~10 the average is noise-prone, and you say so; above ~30 the filters are probably too loose to mean "analogous."

One query pulls the candidates. The bounding-box line is load-bearing: a bare ST_DWithin on geom::geography can't use the spatial index and times out; the && prefilter (expand by ~radius_mi * 0.025 degrees) makes the same query run in under a second, and the exact geography check then trims the box's corners:

SELECT w.well_api, w.operator, w.first_prod_date, w.lateral_length_ft,
       ROUND(w.total_proppant_lbs / NULLIF(w.lateral_length_ft, 0)) AS lbs_ft
FROM public.wells w,
     (SELECT geom FROM public.wells WHERE well_api = '05-123-…') s
WHERE w.geom && ST_Expand(s.geom, 15 * 0.025)
  AND ST_DWithin(w.geom::geography, s.geom::geography, 15 * 1609)
  AND w.formation = 'NIOBRARA'
  AND w.first_prod_date >= CURRENT_DATE - INTERVAL '5 years'
  AND w.well_api <> '05-123-…'

Pads. Forecasting a pad that's about to go in? Skip individual analogs and find analogous pads — averaging whole pads smooths out single-well noise (allocation swaps between pad-mates cancel in the sum). In the DJ, pads are pre-computed: features.well_assignments maps any well to its cohort, and features.cohorts / features.subcohorts carry the group rollups, completion stats, and parent/infill context — join there before deriving anything yourself. Outside the DJ there is no pad column; infer pads as same-operator wells clustered tightly with first production within a month or two of each other.

Reading the parameters

Peak-align the set: chop each well's ramp-up months and line the wells up at their peaks, then average. prod_month (1 = the well's first reported month) makes the alignment mechanical:

WITH peaks AS (
  SELECT DISTINCT ON (well_api) well_api, prod_month AS peak_m
  FROM public.production WHERE well_api IN (…)
  ORDER BY well_api, oil_bbl DESC)
SELECT p.prod_month - k.peak_m AS months_from_peak,
       COUNT(*) AS n_wells,
       AVG(p.oil_bbl)::int AS oil_bbl, AVG(p.gas_mcf)::int AS gas_mcf
FROM public.production p JOIN peaks k USING (well_api)
WHERE p.well_api IN (…)   -- repeat the list; keeps the planner on the
  AND p.prod_month >= k.peak_m   -- index and inside run_sql's time cap
GROUP BY 1 ORDER BY 1

Read the averaged stream like a single well and take the hyperbolic parameters from it: the aligned peak sets the level, the early months set Di, the flattening sets b. The fit is subject to the same transient discipline as any fit — a stack of two-year-olds is transient evidence no matter how many wells are in it, so the priors table and the set's oldest members still own the tail. And watch n_wells: where the stack thins, the average has quietly become a different, older population.

Normalizing. Dividing rates by lateral length, averaging per-foot, and rescaling to the subject's lateral is common practice; know it, and do it if the user asks. It is not the default here — the rate-to-length relationship is not linear. Prefer filtering to comparable laterals so the raw averages compare directly.

Applying it

The analog curve commits like any other forecast: anchor where the subject's forecast starts and read qi there; the subject's own months, if it has any, scale the level up or down. Same math for a 36-month producer and an undrilled location — only the evidence mix changes.

Recording the cohort

Whenever the analog method carried the parameters, record the cohort structurally in the forecast_wells entry — the analog_cohort field: the curve_label (permits sharing a curve share the label), the criteria in plain terms, the kept APIs, and the candidates you excluded with a reason each ("different formation", "lateral outside band", "pre-2019 vintage", "beyond 3 mi"). This is the same judgment the rationale narrates, in a form the server can verify and hydrate: the deal sheet shows your kept analogs' real production against your curve and your exclusion reasons next to them, so the buyer can check the cohort instead of trusting it. Kept analogs must exist and have reported production — the call bounces otherwise, which also catches a mistyped API before it becomes fake evidence.

Wells not yet producing: level, shape, and timing

A DUC or permitted location gives you no history — questions 1 and 2 collapse to "there is none; source everything from the population." Three assertions replace them:

• Level and shape come from the analog method, run in full: build the set, peak-align, and take qi, Di, and b from the averaged stream (an infill child typically runs below its parents — say where the subject sits inside its set). No uptime_factor on a well that hasn't produced: there is no record to compute one from, so just assume it runs.
• Timing is yours too. anchor_month for a non-producer is the asserted first-production month, and it can swing a PUD-heavy deal more than any decline parameter. Source it in order of quality: the deal itself (broker deck rig lines, AFE dates, stated development plans — and when it's material and absent, ask the user); the operator's observable cadence (run_sql: how fast this operator's recent wells went from permit to first production in this county); and only then default norms — a DUC ~18 months out, a permit ~36, adjusted for visible activity (an active rig on the pad vs. a permit aging toward expiry).
• The server dates the drilling capex at your asserted online month and discounts the well in its status bucket; the echo for these wells leads with online_month and has no trailing comparison — EUR/ft against the offset family is the sanity anchor that remains.

Gas and water

Gas gets its own (qi, Di, b) — the same six questions on the gas stream. The physics that links the streams: in tight oil, GOR runs roughly flat while the reservoir is above bubble point, then climbs — often starting within the first year or two. A climbing GOR means gas holds up while oil declines: expect the gas Di you assert to run shallower than the oil Di, and say what GOR behavior you read in the history. A fixed gas-oil ratio is the one assumption you know is wrong. An abrupt GOR spike with a rate drop is operations (lift trouble, choke change), not depletion.

Water is never valued and never committed, but when the dataroom carries it, read it as a diagnostic: early declining water is frac-load flowback and normal; a step up in water with an oil dip is the classic frac-hit fingerprint; a slow monotonic rise speaks to aquifer or flood advance. (The production database carries oil and gas only.)

The rationale

Every committed forecast records, in plain language:

1. months struck (or averaged through) and why
2. the trust judgment — cleanliness, maturity, and any regime breaks
3. qi + anchor and where they came from, and the uptime factor applied
4. Di and its source
5. b and the population it came from, and where the terminal switch lands
6. for a non-producer: the asserted first-production month and its source (deal-stated / operator cadence / default norm)
7. what the echo showed — including whether the run-out EUR is believable — and what you revised in response

Written so another engineer could disagree with a specific line. It cites this well's own months and its population — never the worked examples. When the analog method carried parameters, the rationale names the set: the filters, the count, and any starvation moves (a widened radius, mixed formations).

Parameter priors

Bands plays actually exhibit — starting points and sanity rails, not answers. The deal's own offsets always outrank this table.

Population	b	Year-1 effective decline
Conventional, boundary-dominated	0–0.5	5–30%
Shale / tight oil (Permian, Bakken, Eagle Ford)	~0.9–1.5	~50–95%
Shale gas (Marcellus, Haynesville)	~0.8–1.6	~55–100%

Maturity flattens everything: tight-well declines converge toward roughly 15–20%/yr by year five regardless of basin, then grind down toward the terminal rate. Newer, higher-intensity completions run higher qi and steeper Di with similar or lower b; infill children typically run lower qi than their parents. A forecast whose year-5 behavior contradicts these shapes needs a reason.

Worked examples

Demonstrations of the procedure — same questions, different evidence, different conclusions. Not templates. Never argue from them.

A clean, well-behaved history — trust the well

A producing well, ~10,000-ft lateral, three years on.

1. Evidence. Smooth decline from peak. One dip fifteen months in — downtime, struck, along with the mild flush month after. The long tail since is stable, well-behaved signal.
2. Trust. High — three years of clean decline is the pole where you deliberately overemphasize the historical data. The well is telling you its decline.
3. qi/anchor. Placement almost irrelevant because the well's own trend carries the forecast: the trendline value at a stable spot six to eight months back. Not the peak, not a single reported month.
4. Di. From the stable window forward. The steep early decline is transient — outside the window; never fit the whole life of the well.
5. b. In the band the population supports; the smooth tail corroborates rather than contradicts it, and the echo's terminal switch lands at a believable age.
6. Echo. Implied next-12 landed modestly below trailing-12, year-1 effective decline in family for the formation at this maturity. Committed.

An operationally contaminated history — don't

A producing well, ~15,000-ft lateral, two years on.

1. Evidence. A mess: wild early swings, a mid-life plateau, a trough, a recovery spike, falling again. Most months are evidence of operations, not capacity. Fitting this history makes no sense.
2. Trust. Low. The history says roughly what the well can do — the plateau, the averaged recent level — not how it will decline. Two years on, the population would own b and the tail even if the data were clean.
3. qi/anchor. Roughly the average of the last six months, averaging through the trough and the spike rather than striking them, anchored at the forecast start. Partly visual.
4. Di. Not from this well. From offsets whose current regime looks like this well's near future, plus judgment.
5. b. Same — population and offsets, not a fit.
6. Echo. Checked that implied next-12 sat sensibly against the trailing average given the struck noise, and that the decline profile matched the offsets'. Committed.

A history with a break in it — re-initialize

A producing well, ~7,500-ft lateral, four years on.

1. Evidence. Two and a half years of clean decline, then three months of near-zero — the operator completed offsets one section over — then a recovery that stabilized about 30% below where the old trend projects. The dip-and-recovery is struck; the question is what the stabilized level means.
2. Trust. Split by segment. The pre-hit history is clean and long — trustworthy — but it describes a well that no longer exists. Six months of post-hit data parallel the old trend at the lower level: the well took permanent damage and settled into a new regime. Fit the regime that persists.
3. qi/anchor. From the post-hit trendline — the new level, anchored at the forecast start. Not the old curve's projection, and not an average that smears the trough into the level.
4. Di. The post-hit months are few but parallel to the pre-hit slope, and the pre-hit history is this same rock at the same maturity — so the pre-hit trend sets the slope, applied to the new level.
5. b. Unchanged by the hit: population and mature offsets, same as ever.
6. Echo. Implied next-12 came in well under trailing-12 — correct, since trailing-12 includes seven pre-hit months at the higher level. Verified next-12 against an annualized read of the post-hit months instead, and the decline profile against offsets. Committed.

Barely any history — the analog set carries it

A producing well, ~10,000-ft lateral, one month on.

1. Evidence. One reported month — likely partial, not knowably the peak. It says the well is online and roughly at what scale; it cannot say level, slope, or shape.
2. Trust. Near zero on shape, a sliver on level. This is the population end of the continuum: the analog method runs in full.
3. qi/anchor. Fourteen analogs inside 12 miles — same formation, first production within five years, comparable laterals and proppant per foot. Peak-aligned and averaged. The subject's one month sat modestly below what the analogs' first months typically did, so the level came down proportionally. Anchored at the expected peak — month two — with qi read from the scaled curve there. No uptime factor: the well is brand new; assume it runs.
4. Di. The aligned average's early decline.
5. b. The priors band, checked against the flattening of the set's oldest members.
6. Echo. No trailing-12 exists to compare against, so EUR/ft against the analog family was the sanity anchor — it landed inside the family's range, and the terminal switch landed at a believable age. Committed, with the rationale naming the set and the scale-down.