模型代码增加参数

ML/nmWTAI-ML/scripts/compare_single_case.py

@@ -63,6 +63,7 @@ DEFAULT_SINGLE_CASE = {
         "wellboreC": 0.01,
         "phi": 0.0245,
         "h": 9.144,
+        "Ct": 1.0e-3,
         "Cf": 0.0004315,
     },
 }
@@ -100,6 +101,7 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--wellboreC", type=float, default=DEFAULT_SINGLE_CASE["params"]["wellboreC"])
     parser.add_argument("--phi", type=float, default=DEFAULT_SINGLE_CASE["params"]["phi"])
     parser.add_argument("--h", type=float, default=DEFAULT_SINGLE_CASE["params"]["h"])
+    parser.add_argument("--Ct", type=float, default=DEFAULT_SINGLE_CASE["params"]["Ct"])
     parser.add_argument("--Cf", type=float, default=DEFAULT_SINGLE_CASE["params"]["Cf"])
     parser.add_argument(
         "--well-index",
@@ -301,6 +303,7 @@ def build_params_from_args(args: argparse.Namespace, cfg: Config, schedule: Sche
         wellboreC=float(args.wellboreC),
         phi=float(args.phi),
         h=float(args.h),
+        Ct=float(args.Ct),
         Cf=resolve_cf(args, cfg),
         schedule=schedule,
     )
@@ -412,7 +415,7 @@ def predict_surrogate_curve(
     # 构建参数特征向量
     # 顺序必须与训练阶段保持一致
     params_vec = np.asarray(
-        [params.k, params.skin, params.wellboreC, params.phi, params.h, params.Cf],
+        [params.k, params.skin, params.wellboreC, params.phi, params.h, params.Ct, params.Cf],
         dtype=np.float32,
     ).reshape(1, -1)
     # 将流量制度 schedule 编码成模型输入向量
@@ -464,7 +467,7 @@ def plot_comparison(
     fig.suptitle(
         "Solver vs Surrogate\n"
         f"k={params.k:.6g}, skin={params.skin:.6g}, wellboreC={params.wellboreC:.6g}, "
-        f"phi={params.phi:.6g}, h={params.h:.6g}, Cf={params.Cf:.6g}\n"
+        f"phi={params.phi:.6g}, h={params.h:.6g}, Ct={params.Ct:.6g}, Cf={params.Cf:.6g}\n"
         f"sectionIndex={schedule.sectionIndex}, timeQ={schedule.timeQ}, q={schedule.q}\n"
         f"use_schedule={use_schedule}, model={model_path.name}, "
         f"Overall RMSE={overall['rmse']:.4f}, MAE={overall['mae']:.4f}, "
@@ -578,6 +581,7 @@ def main() -> None:
             "wellboreC": params.wellboreC,
             "phi": params.phi,
             "h": params.h,
+            "Ct": params.Ct,
             "Cf": params.Cf,
         },
         "schedule": {

ML/nmWTAI-ML/scripts/q_sweep_local_ranking.py

@@ -50,6 +50,7 @@ THETA_STAR = {
     "wellboreC": 0.03024150790038185,
     "phi": 0.2736409306526184,
     "h": 21.499309979069142,
+    "Ct": 1.0e-3,
     "Cf": 4.315e-4,
 }
 
@@ -91,6 +92,7 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--wellboreC", type=float, default=THETA_STAR["wellboreC"])
     parser.add_argument("--phi", type=float, default=THETA_STAR["phi"])
     parser.add_argument("--h", type=float, default=THETA_STAR["h"])
+    parser.add_argument("--Ct", type=float, default=THETA_STAR["Ct"])
     parser.add_argument("--Cf", type=float, default=THETA_STAR["Cf"])
     return parser.parse_args()
 
@@ -120,6 +122,7 @@ def make_theta_params(args: argparse.Namespace, schedule: Schedule) -> Params:
         wellboreC=float(args.wellboreC),
         phi=float(args.phi),
         h=float(args.h),
+        Ct=float(args.Ct),
         Cf=float(args.Cf),
         schedule=schedule,
     )
@@ -469,6 +472,7 @@ def main() -> None:
                     "wellboreC": cand.wellboreC,
                     "phi": cand.phi,
                     "h": cand.h,
+                    "Ct": cand.Ct,
                     "Cf": cand.Cf,
                     "solver_objective": solver_obj["dual_log_objective"],
                     "solver_p_obj": solver_obj["log_pressure_objective"],
@@ -490,6 +494,7 @@ def main() -> None:
                         "wellboreC": cand.wellboreC,
                         "phi": cand.phi,
                         "h": cand.h,
+                        "Ct": cand.Ct,
                         "Cf": cand.Cf,
                     }
                 )
@@ -542,6 +547,7 @@ def main() -> None:
             "wellboreC": float(args.wellboreC),
             "phi": float(args.phi),
             "h": float(args.h),
+            "Ct": float(args.Ct),
             "Cf": float(args.Cf),
         },
         "n_q_cases_generated": int(len(q_cases)),

ML/nmWTAI-ML/scripts/replay_pso_trace_screening.py

@@ -39,7 +39,7 @@ from src.data.params import Params, Schedule
 from src.evaluation.autofit_objective import dual_log_objective
 
 
-PARAM_COLUMNS = ["k", "skin", "wellboreC", "phi", "h", "Cf"]
+PARAM_COLUMNS = ["k", "skin", "wellboreC", "phi", "h", "Ct", "Cf"]
 DEFAULT_KEEP_FRACS = [0.5, 0.6, 0.7]
 
 
@@ -165,6 +165,7 @@ def params_from_row(row: dict) -> Params:
         wellboreC=values["wellboreC"],
         phi=values["phi"],
         h=values["h"],
+        Ct=values["Ct"],
         Cf=values["Cf"],
     )
 

ML/nmWTAI-ML/scripts/score_pso_candidates.py

@@ -34,7 +34,7 @@ from src.data.params import Params, Schedule
 from src.evaluation.autofit_objective import dual_log_objective
 
 
-PARAM_COLUMNS = ["k", "skin", "wellboreC", "phi", "h", "Cf"]
+PARAM_COLUMNS = ["k", "skin", "wellboreC", "phi", "h", "Ct", "Cf"]
 
 
 def parse_args() -> argparse.Namespace:
@@ -44,7 +44,7 @@ def parse_args() -> argparse.Namespace:
         "--candidates",
         required=True,
         type=str,
-        help="候选粒子 CSV，包含 particle_id,k,skin,wellboreC,phi,h,Cf",
+        help="候选粒子 CSV，包含 particle_id,k,skin,wellboreC,phi,h,Ct,Cf",
     )
     parser.add_argument(
         "--trace-meta",
@@ -127,6 +127,7 @@ def params_from_row(row: dict) -> Params:
         wellboreC=to_float(row.get("wellboreC")),
         phi=to_float(row.get("phi")),
         h=to_float(row.get("h")),
+        Ct=to_float(row.get("Ct")),
         Cf=to_float(row.get("Cf")),
     )
 

ML/nmWTAI-ML/scripts/validate_autofit_local_ranking.py

@@ -44,6 +44,7 @@ DEFAULT_CASE = {
         "wellboreC": 0.01,
         "phi": 0.0245,
         "h": 9.144,
+        "Ct": 1.0e-3,
         "Cf": 0.0004315,
     },
 }
@@ -82,6 +83,7 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--wellboreC", type=float, default=DEFAULT_CASE["params"]["wellboreC"])
     parser.add_argument("--phi", type=float, default=DEFAULT_CASE["params"]["phi"])
     parser.add_argument("--h", type=float, default=DEFAULT_CASE["params"]["h"])
+    parser.add_argument("--Ct", type=float, default=DEFAULT_CASE["params"]["Ct"])
     parser.add_argument("--Cf", type=float, default=DEFAULT_CASE["params"]["Cf"])
     return parser.parse_args()
 
@@ -151,6 +153,7 @@ def build_params_from_args(args: argparse.Namespace, schedule: Schedule) -> Para
         wellboreC=float(args.wellboreC),
         phi=float(args.phi),
         h=float(args.h),
+        Ct=float(args.Ct),
         Cf=float(args.Cf),
         schedule=schedule,
     )
@@ -197,7 +200,7 @@ def predict_surrogate_curve(
     param_transform = param_feature_transform_from_meta(processed.get("meta", {}))
 
     params_vec = np.asarray(
-        [params.k, params.skin, params.wellboreC, params.phi, params.h, params.Cf],
+        [params.k, params.skin, params.wellboreC, params.phi, params.h, params.Ct, params.Cf],
         dtype=np.float32,
     ).reshape(1, -1)
     schedule_vec = build_schedule_vector(cfg, schedule).reshape(1, -1)
@@ -314,6 +317,7 @@ def _sample_local_candidates(cfg: Config, base: Params, n: int, seed: int, span_
         "wellboreC": float(base.wellboreC),
         "phi": float(base.phi),
         "h": float(base.h),
+        "Ct": float(base.Ct),
         "Cf": float(base.Cf),
     }
 
@@ -352,6 +356,7 @@ def _sample_local_candidates(cfg: Config, base: Params, n: int, seed: int, span_
                 wellboreC=cand["wellboreC"],
                 phi=cand["phi"],
                 h=cand["h"],
+                Ct=cand["Ct"],
                 Cf=cand["Cf"],
                 schedule=base.schedule,
             )
@@ -367,6 +372,7 @@ def _params_to_dict(params: Params) -> dict:
         "wellboreC": float(params.wellboreC),
         "phi": float(params.phi),
         "h": float(params.h),
+        "Ct": float(params.Ct),
         "Cf": float(params.Cf),
     }
 
@@ -487,6 +493,7 @@ def main() -> None:
                         "wellboreC": cand.wellboreC,
                         "phi": cand.phi,
                         "h": cand.h,
+                        "Ct": cand.Ct,
                         "Cf": cand.Cf,
                         "solver_objective": solver_obj["dual_log_objective"],
                         "solver_p_obj": solver_obj["log_pressure_objective"],
@@ -505,7 +512,7 @@ def main() -> None:
                     "[warn] candidate attempt "
                     f"{attempt_idx} skipped: {exc} | "
                     f"k={cand.k:.6g}, skin={cand.skin:.6g}, C={cand.wellboreC:.6g}, "
-                    f"phi={cand.phi:.6g}, h={cand.h:.6g}, Cf={cand.Cf:.6g}"
+                    f"phi={cand.phi:.6g}, h={cand.h:.6g}, Ct={cand.Ct:.6g}, Cf={cand.Cf:.6g}"
                 )
             finally:
                 if shared_runner is None:
@@ -544,6 +551,7 @@ def main() -> None:
                 "wellboreC": target_params.wellboreC,
                 "phi": target_params.phi,
                 "h": target_params.h,
+                "Ct": target_params.Ct,
                 "Cf": target_params.Cf,
             },
             "schedule": {
@@ -580,6 +588,7 @@ def main() -> None:
                     "wellboreC",
                     "phi",
                     "h",
+                    "Ct",
                     "Cf",
                     "solver_objective",
                     "solver_p_obj",
@@ -600,7 +609,7 @@ def main() -> None:
         with open(failed_csv_path, "w", newline="", encoding="utf-8") as f:
             writer = csv.DictWriter(
                 f,
-                fieldnames=["attempt_id", "reason", "k", "skin", "wellboreC", "phi", "h", "Cf"],
+                fieldnames=["attempt_id", "reason", "k", "skin", "wellboreC", "phi", "h", "Ct", "Cf"],
             )
             writer.writeheader()
             writer.writerows(failed_rows)

ML/nmWTAI-ML/scripts/validate_autofit_local_ranking_batch.py

@@ -255,7 +255,7 @@ def main() -> None:
         print(
             f"[target {target_id:03d}] "
             f"k={target_params.k:.6g}, skin={target_params.skin:.6g}, "
-            f"C={target_params.wellboreC:.6g}, phi={target_params.phi:.6g}, h={target_params.h:.6g}, "
+            f"C={target_params.wellboreC:.6g}, phi={target_params.phi:.6g}, h={target_params.h:.6g}, Ct={target_params.Ct:.6g}, "
             f"section={target_params.schedule.sectionIndex}, spans={span_fracs}"
         )
 
@@ -303,6 +303,7 @@ def main() -> None:
                             "wellboreC": cand.wellboreC,
                             "phi": cand.phi,
                             "h": cand.h,
+                            "Ct": cand.Ct,
                             "Cf": cand.Cf,
                             "solver_objective": solver_obj["dual_log_objective"],
                             "solver_p_obj": solver_obj["log_pressure_objective"],
@@ -329,6 +330,7 @@ def main() -> None:
                         "target_wellboreC": target_params.wellboreC,
                         "target_phi": target_params.phi,
                         "target_h": target_params.h,
+                        "target_Ct": target_params.Ct,
                         "target_Cf": target_params.Cf,
                         "target_sectionIndex": int(target_params.schedule.sectionIndex),
                         "target_timeQ_json": json.dumps(target_params.schedule.timeQ),

ML/nmWTAI-ML/src/data/dataset_generation.py

@@ -415,7 +415,7 @@ def _worker_simulate_parallel(args):
             wellboreC=float(params_dict["wellboreC"]),
             phi=float(params_dict["phi"]),
             h=float(params_dict["h"]),
-            Ct=float(params_dict.get("Ct", 1.0e-3)),
+            Ct=float(params_dict["Ct"]),
             Cf=float(params_dict["Cf"]),
             schedule=None,
         )

ML/nmWTAI-ML/src/data/params.py

@@ -73,8 +73,8 @@ class Params:
     wellboreC: float
     phi: float
     h: float
-    Ct: float = 1.0e-3
+    Ct: float
-    Cf: float = 4.315e-4
+    Cf: float
     schedule: Optional[Schedule] = None
 
     def to_dict(self) -> Dict[str, float]:

ML/nmWTAI-ML/src/data/preprocess.py

@@ -187,9 +187,6 @@ def preprocess_dataset(
         schedule_meta_names = _decode_attr_list(f.attrs.get("schedule_meta_names"))
         source_name_vocab = _decode_attr_list(f.attrs.get("source_name_vocab"))
         if param_names is None:
-            if x_params.shape[1] == 6:
-                param_names = ["k", "skin", "wellboreC", "phi", "h", "Cf"]
-            elif x_params.shape[1] == 7:
             param_names = ["k", "skin", "wellboreC", "phi", "h", "Ct", "Cf"]
 
         schedule_meta = _ensure_2d("schedule_meta", f["schedule_meta"][:]) if "schedule_meta" in f else None