Six Standards That Affect Your Pipeline Leak Detection Program
Given the current focus on pipeline safety, leak detection, rupture detection and incident response, I thought it would be useful to summarize the key standards that affect pipeline operators.
API 1155 Evaluation Methodology for Software Based Leak Detection Systems. 1st Edition (February 1995)
Replaced by API RP 1130, API 1155 defined a uniform methodology that could be employed by pipeline companies as an aid in the evaluation of software-based leak detection systems.
API RP 1130 Computational Pipeline Monitoring for Liquids. Third Edition (2007), reaffirmed in 2012
Written to assist a pipeline operator in identifying issues relevant to the selection, implementation, testing, and operation of leak detection system (LDS). 1130 divides LDS divides into internally based LDS and externally based LDS. Internally based systems utilize field instrumentation (for example flow, pressure or fluid temperature sensors) to monitor internal pipeline parameters. Externally based systems also utilize field instrumentation (for example infrared radiometers or thermal cameras, vapor sensors, acoustic microphones or fiber-optic cables) to monitor external pipeline parameters.
The RP provides tools that assist Pipeline Controllers in detecting commodity releases that are within the sensitivity of the algorithm.
The U.S. Department of Transportation's Office of Pipeline Safety (DOT-OPS) on July 6, 1998, adopted this document into 49 CFR (Code of Federal Regulations) Part 195, the federal rules that are intended to ensure safe operation of the nation's hazardous liquids pipelines. Beginning July 6, 1999, all operators of U.S. hazardous liquids pipelines engaged in pipeline leak detection known as "computational pipeline monitoring" (CPM) must use, by reference and with other information, the document API 1130.
(§ 195.134 CPM leak detection).
This section of the regulation applies to every hazardous liquid pipeline transporting liquid in single phase (without gas in the liquid). Each new computational pipeline monitoring (CPM) leak detection system on these pipelines and each replaced component of an existing CPM system must comply with section 4.2 of API 1130 in its design and with any other design criteria addressed in API 1130 for components of the CPM leak detection system.
Operators who have no computer-based, leak-detection system are not required to install one. But those currently running such a system or installing one in the future must consult API 1130 in designing, evaluating, operating, maintaining, and/or testing their CPM systems.
API 1175 Pipeline Leak Detection Program Management. First Edition (2015)
In December of 2015, the American Petroleum Institute issued the first edition of Recommended Practice 1175; Pipeline Leak Detection Program. This document provides a broad stroke, high-level view to pipeline operators of liquid pipeline leak detection programs (LDPs). The recommendations satisfy current US pipeline regulations and encourage pipeline operators to “go beyond", that is to strive for better utilization of LDPs in hazardous liquid pipelines. The overall goal is to detect leaks quickly and with certainty, facilitating quicker shutdown to minimizing negative consequences.1
The API and the Association of Oil Pipelines issued this document in response to the US Department of Transportation Pipeline and Hazardous Materials Safety Administration (PHMSA) request to further address pipeline leak detection effectiveness.
As previous API documents addressing pipeline leak detection are frequently cited or even adopted by numerous regulating authorities around the world to set pipeline leak detection standards, it is likely that this new RP will also influence leak detection regulation around the world.
Main sections of Recommended Practice 1175
- Leak detection culture and strategy
- Performance targets, metrics, and KPIs
- Control Center Procedures
- Alarm Management
- Roles responsibilities & training
- Performance evaluation of the leak detection plan
- Management of change
- Improving planning and process
Read our "A look inside API Recommended Practice 1175" eigh-part series here
API/AOPL White Paper 12, Liquid Pipeline Rupture Recognition and Response, August 2014
Some high-profile liquid pipeline rupture incidents highlighted that operators can improve in more consistently recognizing and responding to high flow rate pipeline releases, often referred to as ruptures. Stakeholders expect rupture focused pipeline monitoring systems and robust operating procedures that align with a strong "Think Rupture" culture throughout a pipeline operator's organization. This document provides guidance to operators to ensure prompt and consistent detection and response to ruptures. This guidance is based on a composite of practices and shared knowledge on rupture tools and techniques being used within the liquid pipeline industry. This document provides an overview of key concepts for consideration in rupture detection and response.
API RP 1168 Pipeline Control Room Management, Second Edition (2015)
This RP provides pipeline operators, and pipeline Controllers with guidance on industry best practices on control room management to consider when developing or enhancing processes, procedures, and training. This document addresses pipeline safety elements in Pipeline Control Rooms for hazardous liquid and natural gas pipelines in both the transportation and distribution sectors:
- Personnel roles, authorities, and responsibilities
- Guidelines for shift turnover
- Provide adequate information
- Fatigue mitigation
- Change management
- Operating experience
- The workload of pipeline Controllers
API 1149 Pipeline Variable Uncertainties And Their Effects On Leak Detectability. First Edition (1993)
1149 deals with the uncertainties of various elements of the pipeline and how they affect leak detectability. Publication 1149 uses a simple mass balance technique to come up with a theoretical leak detection limit taking into account instrument inaccuracies and physical characteristics of a pipeline.
Leak detection sensitivity >= Steady state uncertainty + Transient uncertainty
Where Steady state uncertainty is uncertainty in flow caused by flow, temperature and pressure uncertainties and Transient uncertainty being the specific uncertainty caused by transient conditions of a line.
This publication remains valid and extremely valuable within its range of applicability. Generally speaking, it is designed for crude oil and refined products pipelines. It is very limited as it does not account for temperature and is only for steady state operation. It also only focuses on (while also discussing other ancillary issues) single, straight pipelines and on the Material Balance method of CPM, particularly under steady state conditions.
API 1149 Ed. 2 (2015), Pipeline Variable Uncertainties And Their Effects On Leak Detectability
The 1993 API publication 1149 had several shortcomings and gaps in justifying the sensitivity estimation metrics. The revision addresses these to improve the industry’s ability to qualify sensitivities estimations of many CPM applications:
1) The need to cover the complete range of CPM methods in current, practical use, - not just volume balance internally based CPM systems
2) The need to extend the assessment to Highly Volatile Liquids (HVL) and natural gasses
3) Alignment of the definitions and approach to uncertainty with those used systematically in instrument and measurement engineering
4) Recognition of the nonlinear and strongly time-dependent nature of certain engineering factors
5) Inclusion, in detail, of a number of engineering factors that occur regularly in pipeline operations
6) The inclusion of the major telemetry (SCADA) uncertainties in measurement
Section 7 provides guidance on the source uncertainties present in different types of sensors and measurement systems. It also discusses what happens to the presumed normally distributed errors, pertaining to forecasting false alarm estimates, given that these alarms occur due to a failure in the telemetry system, which in turn would be outside of a normal statistical probability. In addition, it addresses the issues related to how this does not readily relate to false alarm rate, particularly with consideration of these statistical outliers.