Preview Wellbore Tubing Records

First 100 rows

Out of roughly 467.2K rows in texas.wb_tubing. Version obs-4ec00ac2ecc9.

`api_county`	`api_unique`	`segment_counter`	`tubing_inches`	`tubing_frac_num`	`tubing_frac_denom`	`depth_set`	`packer_set`	`ingested_at`
001	00037	1	2	7	8	5905	null	May 17, 2026, 10 p.m.
001	00045	1	2	3	8	1000	null	May 17, 2026, 10 p.m.
001	00046	1	2	3	8	1000	null	May 17, 2026, 10 p.m.
001	00070	1	2	7	8	9726	9726	May 17, 2026, 10 p.m.
001	00071	1	2	3	8	9916	9920	May 17, 2026, 10 p.m.
001	00072	1	2	3	8	10063	10060	May 17, 2026, 10 p.m.
001	00078	1	2	3	8	5941	5934	May 17, 2026, 10 p.m.
001	00084	1	2	3	8	9905	9905	May 17, 2026, 10 p.m.
001	00087	1	2	3	8	null	null	May 17, 2026, 10 p.m.
001	00089	1	2	3	8	10065	10026	May 17, 2026, 10 p.m.
001	00090	1	2	3	8	9823	9820	May 17, 2026, 10 p.m.
001	00091	1	2	3	8	9935	9932	May 17, 2026, 10 p.m.
001	00091	2	null	null	null	null	9832	May 17, 2026, 10 p.m.
001	00092	1	2	3	8	9897	9813	May 17, 2026, 10 p.m.
001	00092	2	null	null	null	null	9832	May 17, 2026, 10 p.m.
001	00092	3	null	null	null	null	9892	May 17, 2026, 10 p.m.
001	00094	1	2	7	8	4858	null	May 17, 2026, 10 p.m.
001	00094	2	2	3	8	null	9940	May 17, 2026, 10 p.m.
001	00101	1	2	7	8	5022	5022	May 17, 2026, 10 p.m.
001	00106	1	2	7	8	9996	9996	May 17, 2026, 10 p.m.
001	00110	1	null	null	null	null	9850	May 17, 2026, 10 p.m.
001	00111	1	2	7	8	4902	4902	May 17, 2026, 10 p.m.
001	00112	1	2	7	8	9827	9342	May 17, 2026, 10 p.m.
001	00112	2	null	null	null	null	9821	May 17, 2026, 10 p.m.
001	00113	1	2	7	8	5048	5048	May 17, 2026, 10 p.m.
001	00117	1	2	7	8	9715	9715	May 17, 2026, 10 p.m.
001	00120	1	2	null	null	9829	null	May 17, 2026, 10 p.m.
001	00128	1	2	3	8	6193	6193	May 17, 2026, 10 p.m.
001	00135	1	2	3	8	9919	9867	May 17, 2026, 10 p.m.
001	00135	2	2	3	8	null	9911	May 17, 2026, 10 p.m.
001	00137	1	2	3	8	9830	9830	May 17, 2026, 10 p.m.
001	00167	1	2	7	8	2453	null	May 17, 2026, 10 p.m.
001	00240	1	2	3	8	3720	null	May 17, 2026, 10 p.m.
001	00260	1	2	7	8	4549	4549	May 17, 2026, 10 p.m.
001	00261	1	2	3	8	4523	4523	May 17, 2026, 10 p.m.
001	00262	1	2	3	8	1750	null	May 17, 2026, 10 p.m.
001	00264	1	2	3	8	4569	null	May 17, 2026, 10 p.m.
001	00265	1	2	3	8	4550	4550	May 17, 2026, 10 p.m.
001	00267	1	2	3	8	4450	null	May 17, 2026, 10 p.m.
001	00270	1	2	3	8	4534	4534	May 17, 2026, 10 p.m.
001	00273	1	2	7	8	4191	4191	May 17, 2026, 10 p.m.
001	00274	1	2	3	8	4507	null	May 17, 2026, 10 p.m.
001	00277	1	2	7	8	4500	null	May 17, 2026, 10 p.m.
001	00280	1	4	1	2	4800	4800	May 17, 2026, 10 p.m.
001	00282	1	2	3	8	4521	4521	May 17, 2026, 10 p.m.
001	00284	1	3	1	2	4000	null	May 17, 2026, 10 p.m.
001	00286	1	2	7	8	4498	4498	May 17, 2026, 10 p.m.
001	00293	1	2	7	8	5180	3880	May 17, 2026, 10 p.m.
001	00295	1	2	3	8	3429	null	May 17, 2026, 10 p.m.
001	00305	1	2	3	8	4487	null	May 17, 2026, 10 p.m.
001	00314	1	3	1	2	4530	4530	May 17, 2026, 10 p.m.
001	00315	1	2	7	8	4007	null	May 17, 2026, 10 p.m.
001	00320	1	2	3	8	4500	4500	May 17, 2026, 10 p.m.
001	00336	1	2	3	8	4595	4595	May 17, 2026, 10 p.m.
001	00337	1	2	7	8	4555	null	May 17, 2026, 10 p.m.
001	00351	1	2	7	8	3052	null	May 17, 2026, 10 p.m.
001	00458	1	2	7	8	4480	null	May 17, 2026, 10 p.m.
001	00501	1	2	3	8	10250	10250	May 17, 2026, 10 p.m.
001	00501	2	2	3	8	9436	9436	May 17, 2026, 10 p.m.
001	00538	1	null	null	null	3163	null	May 17, 2026, 10 p.m.
001	00540	1	2	7	8	3102	null	May 17, 2026, 10 p.m.
001	00545	1	2	7	8	5713	5713	May 17, 2026, 10 p.m.
001	00560	1	2	7	8	4496	4500	May 17, 2026, 10 p.m.
001	00564	1	2	7	8	4430	null	May 17, 2026, 10 p.m.
001	00567	1	2	7	8	4410	4415	May 17, 2026, 10 p.m.
001	00571	1	2	7	8	4479	null	May 17, 2026, 10 p.m.
001	00573	1	2	7	8	4405	null	May 17, 2026, 10 p.m.
001	00578	1	2	3	8	4575	4575	May 17, 2026, 10 p.m.
001	00619	1	2	7	8	9956	null	May 17, 2026, 10 p.m.
001	00631	1	2	3	8	30	null	May 17, 2026, 10 p.m.
001	00631	2	2	3	8	6181	null	May 17, 2026, 10 p.m.
001	00640	1	2	3	8	8680	8730	May 17, 2026, 10 p.m.
001	00666	1	2	3	8	8802	8802	May 17, 2026, 10 p.m.
001	00671	1	2	null	null	8940	null	May 17, 2026, 10 p.m.
001	00674	1	2	null	null	8806	null	May 17, 2026, 10 p.m.
001	00708	1	2	7	8	9654	null	May 17, 2026, 10 p.m.
001	00751	1	2	3	8	9517	null	May 17, 2026, 10 p.m.
001	00757	1	2	7	8	7200	7200	May 17, 2026, 10 p.m.
001	00759	1	2	3	8	9611	9611	May 17, 2026, 10 p.m.
001	00766	1	2	3	8	8127	null	May 17, 2026, 10 p.m.
001	00784	1	2	3	8	8700	8700	May 17, 2026, 10 p.m.
001	00814	1	2	7	8	3000	3003	May 17, 2026, 10 p.m.
001	00815	1	3	1	2	3888	3702	May 17, 2026, 10 p.m.
001	00818	1	2	7	8	3699	3699	May 17, 2026, 10 p.m.
001	00819	1	2	3	8	2256	2248	May 17, 2026, 10 p.m.
001	00840	1	2	3	8	3862	3862	May 17, 2026, 10 p.m.
001	00872	1	2	3	8	3396	3396	May 17, 2026, 10 p.m.
001	00877	1	2	7	8	3834	3834	May 17, 2026, 10 p.m.
001	00880	1	2	7	8	3555	3555	May 17, 2026, 10 p.m.
001	00881	1	2	7	8	3024	3024	May 17, 2026, 10 p.m.
001	00882	1	3	1	2	3865	3773	May 17, 2026, 10 p.m.
001	00883	1	2	7	8	3155	3155	May 17, 2026, 10 p.m.
001	00887	1	2	3	8	7730	7730	May 17, 2026, 10 p.m.
001	00903	1	2	3	8	3890	null	May 17, 2026, 10 p.m.
001	00918	1	4	null	null	1646	null	May 17, 2026, 10 p.m.
001	00951	1	2	3	8	7475	null	May 17, 2026, 10 p.m.
001	00973	1	2	3	8	3978	null	May 17, 2026, 10 p.m.
001	00978	1	2	3	8	4039	4030	May 17, 2026, 10 p.m.
001	01015	1	2	3	8	3144	3144	May 17, 2026, 10 p.m.
001	01023	1	2	7	8	3004	3004	May 17, 2026, 10 p.m.

What is in each column

Sampled, not exhaustive — enough to tell whether this is the data you want without making the database read tens of millions of rows.

`api_county`

varchar

Sampled: 50K
Distinct: 33

003 17.5K
009 5K
013 3.1K
029 3.1K
059 2.2K
049 2K
039 1.9K
051 1.9K
033 1.8K
055 1.6K
041 1.6K
025 1.3K

`api_unique`

varchar

Sampled: 50K
Distinct: 23.6K

32083 12
31648 11
31655 11
30874 10
31635 10
32285 10
30959 10
30956 10
31822 10
30293 10
33312 10
30780 10

`segment_counter`

int2

Sampled: 50K
Distinct: 4

1 49.3K
2 663
3 60
4 5

`tubing_inches`

int4

Sampled: 50K
Distinct: 15
Empty: 0.0257

2 47.1K
3 528
1 403
4 390
7 72
10 45
5 45
9 40
13 35
16 34
8 14
6 11

`tubing_frac_num`

int4

Sampled: 50K
Distinct: 11
Empty: 0.0389

3 28.2K
7 18K
1 1.7K
5 180
2 21
4 3
19 3
9 2
6 1
11 1
8 1

`tubing_frac_denom`

int4

Sampled: 50K
Distinct: 16
Empty: 0.0389

8 45.6K
2 1.3K
4 934
16 225
7 11
6 11
5 7
3 6
38 5
78 4
50 3
9 3

`depth_set`

int4

Sampled: 50K
Distinct: 10.8K
Empty: 0.0225

300 167
1200 166
1100 132
1400 125
1300 109
360 106
340 99
1150 98
1250 97
1450 86
1500 83
1080 79

`packer_set`

int4

Sampled: 50K
Distinct: 8.5K
Empty: 0.6032

1100 53
1200 32
1250 30
1400 26
500 26
1050 23
4500 23
7500 22
1150 22
4600 21
8000 20
1300 20

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