Completion Diagnostics

Diagnostic skill for interpreting frac calibration tests and stage execution data. Best results come from raw pressure, rate, slurry concentration, and proppant time series rather than only stage summaries.

Important: Completion diagnostics fail most often from bad normalization, not bad physics. Before interpreting closure or cluster efficiency, verify time alignment, gauge units, hydrostatic assumptions, pipe friction assumptions, and perforation depth.

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Data To Request First

• Measured treating pressure and rate versus time
• Slurry concentration and proppant schedule versus time
• Shut-in timestamp and first few minutes of pressure falloff
• TVD to perforations and fluid density during treatment
• Surface iron / pipe friction estimate and perforation scheme
• Stage length, cluster count, shots per foot, and entry-hole diameter
• Offset DFIT, minifrac, microseismic, tracer, DAS, or fiber diagnostics if available

If only summary data are available, provide ranges and uncertainty bands rather than a single precise answer.

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Workflow

1. Normalize pressure to perforation depth.
2. Separate hydrostatic, pipe friction, perforation friction, and fracture components.
3. Estimate ISIP from the first shut-in points and bracket closure pressure.
4. Compare net pressure and perforation friction across stages or clusters.
5. Diagnose anomalies from pressure shape, rate response, and slurry behavior.

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Module 1 - Pressure Normalization

def bhp_at_perfs(surface_pressure_psi, tvd_ft, fluid_ppg,
                 pipe_friction_psi=0.0, perf_friction_psi=0.0):
    """
    Bottomhole treating pressure at perforations.
    BHTP = P_surface + hydrostatic + pipe friction + perf friction
    hydrostatic = 0.052 * MW(ppg) * TVD(ft)
    """
    hydrostatic = 0.052 * fluid_ppg * tvd_ft
    return surface_pressure_psi + hydrostatic + pipe_friction_psi + perf_friction_psi

def net_treating_pressure(bhtp_psi, closure_pressure_psi):
    """
    Net pressure above closure / minimum stress.
    For screening, use closure pressure from DFIT or minifrac.
    """
    return bhtp_psi - closure_pressure_psi

def fracture_gradient(pressure_psi, tvd_ft):
    """Pressure gradient in psi/ft."""
    if tvd_ft <= 0:
        return None
    return pressure_psi / tvd_ft

Interpretation ranges

Metric	Typical meaning
Closure gradient 0.65 to 0.85 psi/ft	Common unconventional stress range
Net pressure 200 to 800 psi	Reasonable range for many slickwater stages
Rapid net pressure escalation at constant rate	Near-wellbore restriction, sand loading, or height growth change

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Module 2 - ISIP and Closure Screening

def isip_from_sqrt_time(time_sec, pressure_psi, n_points=8):
    """
    Estimate ISIP by fitting pressure versus sqrt(time) over the first
    post-shut-in points and extrapolating to time = 0.
    """
    import math

    n = min(len(time_sec), len(pressure_psi), n_points)
    if n < 2:
        return None

    x = [math.sqrt(max(t, 0.0)) for t in time_sec[:n]]
    y = pressure_psi[:n]

    x_bar = sum(x) / n
    y_bar = sum(y) / n
    sxx = sum((xi - x_bar) ** 2 for xi in x)
    if sxx == 0:
        return None
    sxy = sum((xi - x_bar) * (yi - y_bar) for xi, yi in zip(x, y))
    slope = sxy / sxx
    intercept = y_bar - slope * x_bar
    return intercept

def closure_pressure_range(isip_psi, pressure_at_slope_change_psi,
                           uncertainty_psi=100.0):
    """
    Return a closure bracket rather than a single number when only simple
    diagnostics are available.
    """
    low = min(isip_psi, pressure_at_slope_change_psi) - uncertainty_psi
    high = max(isip_psi, pressure_at_slope_change_psi) + uncertainty_psi
    return {"low_psi": low, "high_psi": high}

Closure picking hierarchy

1. Use raw DFIT falloff with G-function and square-root-time plots.
2. Cross-check derivative behavior against after-closure linear flow.
3. If only treatment shut-in data exist, give a bounded closure range.
4. Do not present single-psi precision unless the raw falloff quality supports it.

Diagnostic cues

Plot cue	Likely interpretation
Early steep drop immediately after shut-in	Pipe / near-wellbore friction bleeding off
Clear slope change on sqrt-time	Candidate closure point
G-function derivative minimum then rise	Candidate closure region
No clean break	Multiple closures, complexity, poor shut-in data, or height growth

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Module 3 - Step-Rate And Breakdown

def step_rate_slopes(rate_bpm, pressure_psi):
    """
    Piecewise slope dP/dQ between adjacent rate steps.
    In a clean step-rate test, fracture initiation often appears as a
    sustained reduction in slope (injectivity increase).
    """
    slopes = []
    for i in range(1, min(len(rate_bpm), len(pressure_psi))):
        dq = rate_bpm[i] - rate_bpm[i - 1]
        if dq == 0:
            slopes.append(None)
        else:
            slopes.append((pressure_psi[i] - pressure_psi[i - 1]) / dq)
    return slopes

def fracture_initiation_step(rate_bpm, pressure_psi, slope_drop_frac=0.25):
    """
    Identify the first rate step where slope drops materially relative to the
    previous step. This is a screening indicator, not a final picked pressure.
    """
    slopes = step_rate_slopes(rate_bpm, pressure_psi)
    for i in range(1, len(slopes)):
        if slopes[i - 1] and slopes[i] and slopes[i] < (1 - slope_drop_frac) * slopes[i - 1]:
            return {"step_index": i + 1, "pressure_psi": pressure_psi[i + 1]}
    return None

Use cases

• Breakdown pressure: first clear fracture initiation point
• Reopening pressure: later cycle pressure where fracture reopens
• Step-down test: separate surface friction, pipe friction, and perforation friction after treatment

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Module 4 - Cluster Efficiency And Perforation Friction

def perforation_friction_psi(rate_bpm, fluid_ppg, shot_count,
                             entry_diameter_in, discharge_coeff=0.8):
    """
    Limited-entry perforation friction estimate.
    Common field-units screening form:
    dP = 0.2369 * q^2 * rho / (Cd^2 * N^2 * d^4)
    q in bbl/min, rho in ppg, N total effective open holes, d in inches.
    """
    if shot_count <= 0 or entry_diameter_in <= 0 or discharge_coeff <= 0:
        return None
    numerator = 0.2369 * rate_bpm**2 * fluid_ppg
    denominator = discharge_coeff**2 * shot_count**2 * entry_diameter_in**4
    return numerator / denominator

def cluster_efficiency_from_rate_split(cluster_rates_bpm):
    """
    Simple rate-balance metric.
    1.0 means perfectly even distribution.
    """
    if not cluster_rates_bpm:
        return None
    avg_q = sum(cluster_rates_bpm) / len(cluster_rates_bpm)
    if avg_q == 0:
        return None
    return min(cluster_rates_bpm) / avg_q

Rule-of-thumb guidance

Indicator	Screening guidance
Perforation friction < 200 psi	Often too low for robust limited entry
Perforation friction 300 to 800 psi	Common design target range
Cluster efficiency < 0.6	Large imbalance likely
Rising treating pressure with falling rate allocation quality	Near-wellbore restriction or dominant cluster growth

For rigorous cluster allocation, combine this skill with pnge:perforation-design and any available tracer or DAS data.

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Module 5 - Pressure Signature Diagnosis

Pressure behavior	Likely cause	What to check next
Sharp pressure rise at constant rate near tail-in	Screenout or near-screenout	Slurry concentration, sand ramp, blender behavior, heel cluster restriction
High pressure from start of stage	Tortuosity / near-wellbore complexity	Step-down, perf friction, gun phasing, limited-entry sizing
Pressure falls while rate also falls unexpectedly	Surface equipment issue	Pump schedule, iron restriction, data QA
Lower-than-offset pressure with poor response	Out-of-zone growth or stress shadow difference	Geology, stage spacing, offset diagnostics
Oscillatory pressure during proppant pumping	Sand slugs or unstable blender hydration	Slurry density, chemical loading, hydration time

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Output Format

When using this skill, structure the answer as:

1. Pressure normalization assumptions
2. ISIP and closure range
3. Net pressure and fracture gradient interpretation
4. Cluster efficiency / perforation friction findings
5. Most likely operational diagnosis
6. What additional data would materially reduce uncertainty

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Integration Points

• Use pnge:frac-design for geometry and stress context.
• Use pnge:perforation-design for limited-entry and perf friction detail.
• Use pnge:wellbore-stability for stress and fracture containment context.
• Use pnge:production-chemistry when slurry chemistry or breaker cleanup is implicated.